EP 566: Evaluating Warehouse Employee Performance with WorkScore.ai
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Hey, it's Kevin Long with the New Warehouse Podcast, bringing you a new episode today. And on today's episode, I'm going to be joined by Alex Billion, who is the Chief Sales Officer at WorkScore AI. We're also going to be joined here by Rado Brass, who is the co-founder and CEO of WorkScore AI as well. And they are bringing a new

way to score your workers essentially and evaluate employees within the warehouse space and addressing some of the challenges that you would typically face as a leader in a warehouse operation when it comes to those evaluations. And so I'm very interested as a former leader in a warehouse to see how they're doing this and hear about how the solution works overall. So Alex and Rado, welcome to the show. How are you both?

We're doing great. It's nice to be here. Thank you for inviting us. And I'm excited to talk about it. I think our product will be interesting to a lot of people listening to this podcast. And we'll definitely be touching up on some paints that are very familiar to a lot of warehouse owners and workers as well. So yeah, excited to be here. Thank you.

thank you thank you for having this opportunity it's very important for each startup thank you again yeah absolutely and i'm happy to get you guys on uh as i mentioned you know former uh warehouse manager so i definitely had to do some

employee evaluations back in my days. So I'm very interested to hear how you guys are addressing this. So why don't you kind of kick us off here and tell us a little bit about WorkScore AI, what it's all about, and tell us too, because I think this is a fairly new company. So tell us a little bit about how it got started as well and what was kind of the trigger moment to say like, oh, we need to address this problem.

Yeah, I guess I'll start off with a small origin story. So it was originally Rado's idea because he's the one who has the most experience in our team working in warehouses. So he was initially working in a warehouse in Dubai. Then he moved to Canada. He worked in three different warehouses here. And he also has experience working in different e-commerce platforms. So cumulatively, I would say five, six years of experience, right? And yeah, one experience

thing that he noticed in all of these warehouses, regardless of whether it's here or in Dubai or what kind of a warehouse it is, is that they're all losing a pretty big chunk of money due to their employees' performance. And it ultimately boils down to three main points that I would say are recurring issues in all of these warehouses. It's

and which our research later on showed that it's universally relevant to all warehouses. First is a lack of transparency when it comes to the employee's performance. So what I mean by this is that the worker's performance is not really visible for the

the workers themselves and the warehouse supervisors or the managers or whoever is responsible for evaluating them which is causing a lot of demotivation because if you as a worker are working in an environment where there's 400 other people working with you and you know for a fact that

your hard work is not going to be visible to your manager or to your supervisor, it's really hard to stay motivated and actually try to put in the work, especially if you see that other people are not doing that and you are still being paid the same rate as them. Another major issue was that

Because of the same lack of transparency, it was very difficult for the workers to improve on their mistakes because oftentimes the managers would omit these mistakes they would not see. And again, this is especially true for bigger warehouses. Like obviously, if you have a small warehouse where there's only five employees, it's a different picture because you know everyone. But in bigger environments, it's very difficult to spot and see what exactly each person is doing wrong because sometimes

You can have two people, you can have two workers who are both underperforming or who are not doing as well, but their mistakes might be completely different things. For one person, it might be just using the warehouse management system. Maybe they're making mistakes. Therefore, the other person, it might simply be reworking

route optimization issue. So just them taking a more optimal route when they're doing the picking. So there can be different issues. And the fact that you do not have a clear picture of what each person is doing wrong makes it difficult to address these issues and actually help these employees to improve their performance. And lastly, I would say the third big point is overstaff. In all of these warehouses, people just eyeball when it comes to

estimating how much labor do you need to complete a certain number of tasks. So just to put this into an example, let's say you need to do 100 tasks

tasks like 100 items need to be picked up and you may eyeball that you need 30 employees for this. But if you were to have some tool or some objective way of knowing how each employee is performing, you would be able to assign a specific number and this number may be 20. So maybe you're assigning 10 more employees to do this work when it's not necessary.

So all of these issues combined were causing one of these warehouses, which I will not disclose the name just for privacy issues. And they were losing around $130,000 a month. And this is a very big chunk of money, right? If you can save this, you obviously will.

So yeah, about WorkScore AI, the product itself. So what we do, I'll explain very simply. We take two type of data on the employees. It's their movement data and their action data.

And we're feeding this into our AI module, which is objectively evaluating their performance and giving them a work score, which is where the name comes from. And on top of that, it doesn't just give you the score. It also gives you a breakdown of the efficiency indicators that you're being analyzed upon so that you can see,

what exactly you're doing well and what are your strengths and also your weaknesses so that you can know as an employee you know what you should improve on to bring your score up yeah it's very interesting and you know obviously it certainly is a a big problem and i love that you know you know taking this problem and creating a solution from from what he saw firsthand you know working in these these warehouses and

You know, it is certainly something where, you know, the employees like there's still even though there's so much talk about automation and robotics and things like that in the warehouse, there's still so much of a prevalent human element in the warehouse. It's kind of.

You know, you need to have humans in there in, you know, probably like 95% of scenarios within a warehouse environment. And each human in a sense is different, right? Some are more productive, some are less productive. And I think the warehouse industry and environment naturally changes.

you know, as a high turnover rate as well, right? So you have people that are just kind of coming in real quick, don't necessarily care about the work as much, just trying to pick up some extra money maybe, or, you know, they know that they're only going to be there for a month or two months during the peak season or a holiday. And, you know, why are they going to be super productive, right? And I think,

You know, there's another factor in there too, where it's, you know, most places I know I'm, I'm in New Jersey here and we're like densely populated with warehouses and a warehouse worker can kind of just go to the warehouse next door and probably pick up a job if they lose their one job. Right. And, you know, there's always like a hunger for, for more workers. So, you know, there's certainly a challenge there in trying to like figure out what to do and, you know, how to, I think objectively, as you mentioned in there, kind of

their performance. But I'm curious, you know, what kind of metrics as you did further research on this and obviously Redo's, you know, been in the warehouse himself. So, but, you know, what kind of metrics really did you find where, you

you know, super important to these warehouse operators and what were they kind of struggling to measure and understand about the employees so that they could kind of take some action on these things. And like you mentioned, I mean, you know, losing $130,000 a month is, is a lot, right? I mean, you could get like a high level leader for that type of money within a year.

right let alone a month so so tell us a little bit about i mean as you're exploring this and understanding you know what were those kind of metrics that were missing that you know you're trying to to fill in and now measure yeah so i mean there are so many metrics that you can measure like i can share a source that shares 15 different metrics and we were trying to analyze and see which ones would be the most important for us and to addressing these issues

And the main three for us are picking accuracy, or in other words, just basically how accurately they're completing tasks and how little mistakes they're making. The order fulfillment rate, which is basically the speed and how quickly they're able to complete these tasks. And lastly, it's attendance and punctuality, which refers to not only completing

their ability to come into work in time and not leave early, but also their attendance to their assigned and respective work zones. So for us, the way we work specifically when gathering movement data, so in our system, when we're mapping the warehouse, we split it into different work zones because

You know, you have your leisure zone, you might have a zone for specific tasks for a certain amount of employees and all of the tasks that they complete is within this zone. Again, this is more relevant to larger warehouses. And we also track how much time they spend in their respective work zone. And you might have someone who's spending time in a zone of the warehouse where they don't have any tasks.

Obviously, you know, they shouldn't be there during their working hours for no reason. And yeah, we found that these metrics were the most important ones. The main issue I would say is that there are some alternative tools, right? It's not like there are absolutely no tools for evaluations at all. However, here's the main distinction that makes WorkSquare stand out. So these tools are for measuring processes.

WorkSquare AI is for measuring the employees themselves. So what I mean by this is there are tools that can show you how well did this employee complete this task today, for example. So how quickly did he complete this task compared to others? Or let's say there are 10, there is a specific task and you can see which employee completed

completed this task the best time-wise or something else like accuracy-wise. However, you cannot really have a holistic picture of the employee himself with any of these tools. Because let's say you have Steven, a warehouse employee, and

he comes into work and that day, you know, he didn't sleep well. Like he had some issues outside of work, like in his personal life, like maybe he couldn't sleep the whole night because his baby was crying and now he's feeling very tired. Like he wasn't able to do his best that day. So if you're using these alternative tools, you might,

open up a list and you see steven at the bottom of the list and we might falsely conclude that oh steven is not a good employee what is this you know i have people that are doing 70 more work than him today on on this task but is that really the true picture i mean you would need data on steven's performance throughout like at least months

to have a clear idea of whether this person is doing well or not, whether this person is actually putting in the work. Maybe every single other day that Steven comes into work, he is performing well. So it's these minor things that can skew the numbers, that can create a false picture that we're trying to avoid. And in this, we're really on the employee side because we're trying to help them, first off, get the recognition that they deserve.

And second, to make their work visible and transparent for their supervisors so that they don't have to be worried about, oh, you know, I worked so hard these past couple of months, but

There's 500 other people in this warehouse. Did anyone even really notice my work? Like, did it even make a difference? Or, you know, I think my supervisor is a little biased toward me or my team leader is a little biased toward me. I think there is other people that he's friends with. So maybe in his evaluations, even though I'm working the most in his evaluations, maybe he didn't, you know, write me up as a good worker just because he's biased. So we're helping people

the workers be at peace knowing that these are not things that they have to be concerned about anymore because everything is clear everything is transparent hey you're doing your work boom you have your score if you're wondering why it's low or why it's high you have the breakdown a very detailed outline of what exactly you're doing well what exactly you need to work on and it just makes everything easier for everyone

Yeah, yeah. And I think that's such, you know, such great examples there. And I certainly, you know, resonate with a lot of those. I've had a lot of those conversations with employees in my career too as well. And, you know, I think it is, you know, challenging sometimes. Like you said, there's, you know, when you have a ton of employees, right, and you're trying to

you know, measure them all and understand, you know, who's being productive and who's not. I mean, you know, it becomes a, uh, it becomes the numbers essentially. Right. And, you know, you're not thinking about like some of these external factors, like, uh, like our friend, uh, Steven, you mentioned there, uh, that, you know, just had a newborn and it's not getting as much sleep and maybe dragging a little less at work or something like that. And,

I think understanding that, I think, is super important to be able to build something like this to address that in a sense. So I guess tell us a little bit about how it actually works, right? I mean, so where are you taking inputs from? Where are you getting this data from? And then how is it kind of all coming together to...

essentially create this score and then what does that score mean at the end of the day? Our product has come a long way in terms of the technology because we have made some adjustments and

As you yourself mentioned before, initially we're utilizing these bracelets and Bluetooth beacons that we would install within the warehouse to actually track and see the movements of the employees. However, with our IT team recently, we have made, I would say, a huge improvement in that sense because this technology with the Bluetooth beacons, it was very expensive, especially when it comes to

bigger warehouses because you have to install a bunch of them and they're pretty costly

The bracelets themselves were not really an issue, but still having something on you, you know, it can still cause some friction. You know, maybe someone forgets to put on their bracelet or like forgets to put on their bracelet. You know, you never know. So we came up with an alternative, which is much better and does not require any hardware to track people's movements. And now we're able to do that through the cameras within the warehouse. And what we do is we have...

a number assigned for each employee. This is what we do currently in the warehouses where we have our pilot program. So on their uniform, each employee has a number that they're identified by. And through cameras, our AI software is able to accurately track their movements through the warehouse. And this technology has a lot of potential because in the future, we're also aiming to

not only use the cameras for the movements themselves, but also to see their actions. So actually completion of the task. So how they pick an item, like all of that is being recorded through the camera and analyzed through the AI software. Because right now, the way we gather information on what I mentioned before as action data, we do that through the warehouse management system. So

Every single button press on the device, every single scan of an item. So all of that is being gathered, logging in the task, you know, pressing the completion of the task. So at every single step of completing the task, we gathered this information.

And then two of these sources, which are separate, are fed into our AI module, which is able to match the movements to the tasks. Because through warehouse management system, you can also see what tasks each person is assigned. So you're able to match the movement to the actions and you're able to see, or I guess the AI is able to

analyze and see how efficient it is. So whether your movement route is efficient for completing those tasks, whether it's quick, whether it's slow, whether there were some objects maybe on your way that were hindering your movement like that too, through our mapping system, we're able to detect that the AI is able to take into account for that too.

And yeah, that's essentially how it works. So it's these two separate sources of data, but the AI is able to match them together. Interesting there. And so, you know, it's taking, I guess, from the perspective, you know, obviously there's, you know, some data input, like you said, on the process or the task completion, right? That is tracking, right? Which would be like, you know, when you complete a PIC line or a package or something like that, but...

From the movement perspective, I'm curious about this. As you're tracking the movement, how does the AI interpret that movement? What are you really tracking there, that they're having less idle time, or what's the purpose of tracking the movement? We'll be back after a quick break.

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So the main purpose is idle time is also being taken into account, though it's not really a major factor. I would say it is only a major factor for few select employees that do try to slack off and slack.

avoid work. But other than that, I would say for most people, that's not really the main factor. One thing that we do is, root optimization. So making sure that you do, take the best possible route to complete your tasks. And if this is not the case, the software will actually, try to guide you toward this and,

you know, in your indie breakdown, you will be able to see this detailed out at all. You took this route while you could have done this and, you know, this would have saved you time. So this is one thing. Second is, as I mentioned, spending time within your respective work zone during your working hours. So this is another big factor because you

In our pilot programs, we would sometimes have cases. So after we gathered that data, we saw that some people were spending a lot of time outside of their work zone, actually, where they did not really have any tasks or anything.

anything else to do. And yeah, those are those are the two main things. And this data obviously has the most value only after it's matched to the tasks. That's why I was saying these two work together, even though they're completely distinct sources, we mesh them together to see how the movement and the task completion work together. Interesting. Yeah, yeah, I was curious about that. And I'm curious to I mean, as you're

you know, you're tracking these employees. I mean, obviously there's, you know, been a ton of discussion over, you know, the last several years as more of this technology has come in to place, not only in the warehouse, but in the workplace and in general of, you know, how much is too much to be tracking, right? I mean, I think there was an article a while back about

you know, Amazon tracking the amount of time that employees are spending in the bathroom or something. Right. And there was a lot of like controversy about that. So, so, I mean, how do you, how do you kind of address some of that? Because you mentioned you're tracking through the cameras and, you know, they're wearing a number in a sense, in a sense. And that's how like you're identifying, you know, who this, this worker is. I mean,

How do you kind of address like some of those concerns from the employee perspective, whether it's, you know, from a big brother is watching me or, you know, just like a privacy feeling overall? I mean, how do you address that and how do you make sure like, you know, you're not crossing any lines there, I guess, in a sense? Yeah, that's that's a great question. I mean, that's something that we've been discussing.

dealing with since day one. That's one of the biggest, I would say, I don't want to say challenges, but one of the biggest things that we had to always keep in mind. And what I'll say is we are on the employee side. So I would say WorkScore AI is primarily a software for the employees themselves. Like we are made for the benefit of the employees.

So we've taken steps to ensure that workers' privacy is in place and we don't cross any lines and that the software works for the benefit of the employees. And the first thing we did was

Initially, so you know how credit score works, right? So when you go to a financial institution, they can see your score, but they can't really see any of the details. So let's say if you have a very low credit score, you go to a bank, they cannot really see why exactly your credit score is low. Like they can't see, oh, you missed the payment on your credit.

let's say car or your mortgage, which is why your credit score is going down. So it's pretty much a similar thing that we did with WorkScore AI. So your warehouse supervisor or manager or whoever is on the management side of things,

they will be able to see each employee's score, but they will not be able to actually see the details on why their score is low or high. So these details, they're only accessible to the employees themselves so that they know what they're not doing well and they can improve on these things. But the employer or the manager cannot just...

look at Bob's or Stephen's data and be like, oh, why did you spend 17 minutes in the washroom when you were supposed to spend only seven? So we're definitely trying to avoid this. And you yourself mentioned how retaining employees is a huge challenge for a lot of warehouses. And we're trying to help with this. We're making sure that we're not adding stress to the employees' lives. And we're doing the opposite. And we're trying to motivate them to stay and feel appreciated.

So the second thing is in one of our pilot programs, we've implemented this gamification in a sense. So we've implemented bonuses based on the performance. And after this one small change, the number of people that were opposed to being tracked went to virtually zero. So we had basically no people who were opposing it because people were thinking, okay, if I'm being tracked,

on my performance and if they can only see my score and they can I will be actually rewarded for my work which I wasn't before then why would I not want to be tracked by we're not why would I not want to be getting you know cash bonuses for my hard work

And that's what we did. We kind of set this. So basically like top 20% of performance at the end of the month would be getting a cash bonus. And this both drove the performance of the workers up, all of the performers. And it also, as I said, led to

the reduction of, I guess, opposition or rebels. And yeah, it just all lies in the way you implement it. I think the warehouse supervisors and warehouse owners, they all have a lot of flexibility with the tool and it's essentially up to them to treat the employees well. And I think most, because we've talked to

very, very many warehouse representatives and warehouse owners. We've gone to a lot of events and they all understand very well the importance of actually treating your employees well and making sure that they're recognized for their efforts. They all know that, you know, the method of whipping them and, you know, just trying to be super strict, it just doesn't work out because employees end up leaving. Like they don't end up working better. And this is also supported by research.

And this is just one of the basic human needs, being recognized and being appreciated. This is something that doesn't even have to be based on cash or doesn't have to be a monetary incentive. Even just having something like an employee of the month

It makes people feel so much more appreciated and this makes them feel fulfilled at their work. They're able to perform better and they're able to perform knowing that what they're doing is actually first off making an impact and second off

there is visibility to it. You know, like if you are doing better than the other 550 workers, you will have the highest score amongst those 550 workers. So this really makes a huge difference. And we see that in our current pilot program. So we're,

actually excited to bring this into more warehouses and if you can do something that will help both the workers feel better and be more incentivized to work hard and on top of that help the warehouses save money why not do it interesting yeah yeah and I think it's really interesting there too that the incentivization of it and I think that's such a a big factor in you know helping to

create a better work environment and certainly one where, you know, people, you know, earlier in the conversation you mentioned there, like, you know, I've been working hard, I've been doing this and, you know, does anybody even see it, right? And like when you start to bring in that incentivization, I think that that helps there. So I wanted to go back there a second because I wanted to get some clarification on this. So you mentioned that the,

the data itself is visible to the employee but not the employer right so so from the employer perspective if i'm the manager in that warehouse or whatever the case may be i mean what what do i see like with work score i mean do i just see the score or how do i understand like what's going on with my employees and and what's the value there for for me

The way we set it up in our pilot programs right now, there's obviously a lot of flexibility depending on what our customer demands from us. So you're the one paying for the tool, we will set it up for you however you see fit. So for our pilot programs, again, for the privacy concerns, we set it up in a way that the management side, they can only see the scores of the employees.

while the employees themselves on their end can see all the details and the breakdown of the different efficiency indicators. However, if the employers want to see more data, they can request access for it, and they're requesting this access from the employee himself. So on the employee's end, you get a notification or your manager wants to see your details. If you want to share this, you share that. So from our experience, when this mostly happens is

let's say if you have a new employee who is not able to utilize the warehouse management system well and they're making a lot of mistakes so for them what they can do is you know they can go up to their manager and they themselves can just ask for assistance they can't say you know

I have this issue, in case of our pilots, it was more like, my score is low for this. I don't understand why. I think I'm making some mistakes somewhere. So they can share their data with their manager and they can together collaborate and understand what's wrong. And after the access is given, the employer or the manager can see all this data too on the worker.

But obviously, again, this is all very flexible. So if one of our customers asks us to, you know, I want to have access to this data all the time, we do inform them of the privacy concerns that might arise on the worker's end. And even though there are no legal issues with this, because again, we're only gathering information on their performance and we're not gathering any personal data, we still inform them that, you know, we have experience with these pilots,

It just shows that

it works better both for the workers and for you when we set it up this way where you can only see the score and we set up this access system where you actually have to request the worker to share this data. But again, at the end of the day, it's up to them. If they know their workers well, if they think it's not going to cause any issues, you know, be our guest. Yeah. Interesting. Interesting. Yeah. Yeah. I mean, I like that it requests from the employee, like, you know, do they want to share this? Because it,

you know, you certainly, uh, for whatever reason, you know, you get employees that, you know, they feel different ways and, you know, some,

don't care whether you have that visibility or not. And some, you know, do care. And so I think, you know, having that option, I think kind of level sets and creates a, you know, comfort level for, for everybody. So very interesting stuff with you here and learning about work score. And I think, you know, definitely, certainly a challenge that I've experienced in my, my own career. And I think it's, it's interesting how you're going about addressing it. So, um,

You guys recently won two pitch competitions as well. You know, obviously this is an evolving solution, as you mentioned, you know, going from the wearable now to the cameras as well from that perspective. So tell us a little bit about, you know, what the future of WorkScore AI looks like and kind of what's coming up.

We're definitely evolving very quickly. I mean, we only started working on this project in the beginning of this year. So January will mark the first year of WorkScore AI, I guess, our first anniversary. And we've already come from

just being a very good idea, I shall say, to having the MVP and pilot programs and winning two pitch competitions. And we've also already raised money and we're trying to close our first pre-seed round. So I would say we're growing at a very rapid pace and we have a very big project.

coming up that WorkScore AI, I want to say, is ultimately going to evolve into. So I won't share too much of it, but the basic idea is that we're going to try to work with e-commerce platforms as well. And we're going to be this sort of mediator between warehouses and e-commerce platforms, and we're going to help them work better together. So I won't share the idea, but it's something very interesting and it's

I think the bigger picture that works for AI is going to be integrated puzzle piece and

Very interesting. Yeah. Yeah. I'm interested to see how that develops and how that evolves and how that's going to work too. I'm trying to picture that, but very interesting stuff with you here, both around WorkScore AI and appreciate you coming on the show and telling us about this new solution that you've developed. I'm very interested to see how it continues to evolve and grow over time. If people are interested in learning more about WorkScore AI, what's the best way to do that?

Our website has our contact information, so we can always share the demo of the product. We can share how it works. So we're very open with that. And our LinkedIn, we keep it very up to date. So any accomplishments that we have, we always post about them so that the people see how our product evolves.

But yeah, I would say I would highly encourage anyone that is interested in the product and wants to learn more about it to just contact us directly on our website. And yeah, we'll reach back immediately.

All right. Sounds good. And we'll definitely put all that information at thenewwarehouse.com as well and in the show notes so people can easily find it. So Alex and Mado, thank you very much for joining me today on the show. You've been listening to The New Warehouse Podcast with Kevin Lawton. Subscribe and check us out online at thenewwarehouse.com.

Enjoyed this episode? Make sure you are subscribed to the podcast and for more content from The New Warehouse, find us on LinkedIn and YouTube. Links to subscribe can be found in the show notes and for everything The New Warehouse, head to thenewwarehouse.com.