Lee Rector Podcast
Summary
Lee Richter, CEO of labor AI, talks about the problems in the warehouse labor planning process and how he's using artificial intelligence, machine learning, and analytics to solve them. He explains how his software can predict labor needs with 2% accuracy by analyzing work content and using a database of over 1,000 engineered elements. Lee also discusses the benefits of considering factors such as temperature variance, fatigue rates, and performance in labor planning and addresses the gap between sales and warehouse operations. The speaker explains that as someone who has worked in operations, they have to switch between the perspective of the guys on the floor who focus on getting things done and the executives who focus on the financial side. They introduce their product, Labor AI, which helps with planning labor and making recommendations for non-capital changes that can improve efficiency. They also address the issue of visibility in the warehouse, mentioning that traditional solutions like labor management systems (LMS) can be expensive and may not be applicable in multiWe are changing the game for data analysis in the logistics and warehousing industry with Prescriptive Analytics. We take forecasting to another level, predicting not just what will happen, but what you need to do to optimize your warehouse and operations. Our software is customizable for any business size and range of products. Our goal is to remove the noise and comparisons between sites so that executives can truly optimize for their own operations, not just benchmark against others. This podcast episode discusses the key factors that contribute to the success or failure of a warehouse operation. These include factors such as business objectives, metrics, labor productivity, and automation. The guest also introduces his book, "Warehousing 101: A Reference Guide," which aims to provide readers with a comprehensive understanding of warehousing basics and best practices. Lee Richter, CEO of Labor AI and author of "Warehousing 101: A Reference Guide", is interviewed about his insights in warehouse labor management and how his organization utilizes data to improve efficiency. He talks about the challenges of managing warehouse labor and how traditional methods can be outdated and biased. He also discusses the benefits of using data and technology in labor planning and how it can lead to better decision making. Additionally, he shares information about his book, which serves as a comprehensive resource for people
Transcription
Speaker 2
[00.00.07]
All right. Hey what's up everyone? Thank you for tuning in to episode number 24 at the Warehouse Underground podcast, Real Talk or Warehouse DC and FC professionals of all levels around the globe. I'm Ben, the host the show and joining me on the show today is a fantastic guest. His name is Lee Richter. He is the CEO at labor AI, which is simplifying supply chain management. But he has been a stalwart in this industry for over 30 years and has helped operators at all levels and is very, very, very comfortable on the warehouse floor, no matter where it is in the globe. I know he has worked all over the globe, so he also has written a book called warehousing 101, which we'll talk a little bit about at the end, which is very cool, but I'm very excited to talk to him about labor AI because as you know, in this industry, labor is always the biggest spin. But how people go about determining how much they need, how much they don't need, how they calculate that is anybody's guess. It's all over the map varies, varies wildly depending on where you go. So I'm excited to learn about these new tools, artificial intelligence that is coming online to help predict that much better, using machine learning and analytics to create better processes and better programs for the operators out there in terms of labor. And Lee is right on the front lines in that space with labor AI. So with that, I'll bring him on to the stage. Lee, my friend, thank you very much for coming on the Warehouse Underground podcast. How are you today, sir?
Speaker 1
[00.01.49]
I am excellent. Thanks for having me, Ben. I really appreciate this.
Speaker 2
[00.01.53]
Yeah, now you got it. So I teased it in the intro a little bit, and we talked a little bit about this before the show. You, you you're very comfortable in a warehouse. You've been in this industry for ever, for a long time, doing incredible work everywhere. But in your own words, can you just tell the audience about what is your background and the distribution industry?
Speaker 1
[00.02.15]
Uh, well, again, I from an IT perspective in building systems as solutions, uh, for at least the last 30 years, um, whether I was building a WMS, TMS, bringing them to market, uh, we wrote the number one slotting optimization software for warehouses. And basically for the last 20, 25 years, I've operated as a call it management consultant. I wouldn't call it more of a fireman. Uh, for most peoples around the globe, uh, whether, uh, you know, we're in Australia, Asia, North America, South America, if there was a problem, we typically got called in to either help fix the problem or help identify the problem. Yeah.
Speaker 2
[00.02.57]
Uh, there's a lot of, uh, there's a lot of firemen needed out there for for three pls. So, uh, I appreciate that. Now, you're the CEO at labor AI, right? So I would assume by the name that it has a heavy focus on labor. Uh, obviously. But what is it? What's it all about? I mean, what are you doing? How are the users out there utilizing it? And who are those users? I mean, who is kind of your ideal customer that you can help?
Speaker 1
[00.03.30]
Okay. Um, so it is a long, drawn out story, but, uh, in all these years that I've been developing systems and software and marketplaces, um, in the back of my mind, there's always been one area that's never been tackled inside a supply chain. Um, and manufacturing, which is how do I plan for my labor as we've moved from being, you know, direct to store, uh, enclosed distribution networks to the onset of AI and e-commerce and all these things. People want things delivered faster, smaller quantities, smaller order sizes. Um, which means work content doesn't equal activity. In other words, uh, you might have five orders. And if you used to plan based on orders while a store orders a store order, we know what it's in it. And now you could have an order that's one skew, one line at the back of the warehouse. Um, or you can have one one skew with 5000 units. Um, and so that work content changes differently and then. Having ability to plan. This is really the true, uh, we'll call it Achilles heel of most warehouses these days. They either stop too many people to meet their service level requirements because the what if scenario, what happens if we get loaded, um, versus, oh, I've got 30 people here today and I don't have enough work for them. What I do with them, um, and for the typical operator, they don't see that to the morning up. So they basically their time to react and do anything on labor side is if they don't have enough time. So what I wanted to do was build a low cost, rapidly implementable solution that includes things like being able to analyze your orders in a real prompt manner. So in seconds, um, which is what we use the AI agent to do, it actually mimics the allocation system in the WMS. So we're not replacing people with our AI. We're actually replacing the WMS with our AI. And then at the same side of things, none of this works if you don't have a set of standards or engineered standards to drive your your work content. So we build a database of over a thousand engineered elements to then basically say, while stepping on a fork is 1.2 seconds or 1.6 seconds, um, and all these touch points across 37, we'll call it direct touchpoints in your warehouse. So by doing that, we combined the onset of AI with a proprietary database to tell you today it's what time or whatever it is at where you are today with whatever's in your order. Well, I can tell you how much work or how many hours of labor you have across all your touch points tomorrow. Yeah, within 2%. So rather now you're right sizing in advance and we call that prescriptive analytics. We're not seeing Oracle averages every single day. We create a new set of KPIs for the work content that you have in that day. So
Speaker 2
[00.06.33]
is it fair to say that the more you feed the beast, the more inputs go in there, the better and better and more dialed in it can become. Because in my experience, when you're talking about labor planning, it's almost always retroactive. People will look at, hey, well, what what did you spend the year before in this month? What was the sales? What was the widgets? How many labor hours did you use. And okay, let's just kind of build a generic plan from that. But I've always felt like there's so many there's so many gaps in that analysis. Like it's it's not intricate enough. So is that fair to say that you're getting down to the I know you mentioned some some seconds here in your analysis. So is that what you're doing? You're getting down into the nitty gritty of all these data points that a human is just not capable of calculating. And then we'll do it better. You
Speaker 1
[00.07.25]
get Actually, our software is pre-populated with all these numbers, right. So our software works like, you know, a mapping software. So if you're driving from Chicago to New York, the first thing you do is you get up, you look at your phone and you pick up one of the mapping applications and it says, oh, if you go I-80, it's going to take you eight hours and four minutes. And if I said to you, hey, Ben, you're driving from New York, you'll go, oh, you know what usually takes me about 7.5 hours? So that's your historical average. And Google Maps is synthesizing, saying right now, if I use this model, it's eight hours and four minutes, and then you make a decision based on the travel path. So you're getting more accurate information from that mapping software rather than looking at historical averages, because the mapping software looks at, oh, there's an accident, you know, 234 miles, and then there's construction and things like that. So when you look at those mapping solutions, they have five elements. We have a thousand elements. So we're definitely more intricate. But really the the true power of the software is, you know, allowing you to see something before it happens. Right. Instead of being 100% reactive or rear view looking, you're actually looking forward. Um, one of our clients has said we spent, you know, $5 million over the last two years figuring out everything that we've done. And they said, but you know, how much we've spent on what we should have done. Zero. So labor AI is actually giving you the targets today of what you should be doing tomorrow.
Speaker 2
[00.08.59]
Yeah. And I've heard you talk about this and and when I heard you mention this, I thought, okay, he he's really doing a deep dive here. I've heard you talk about the temperature variance depending on the facility, if it's super hot or super cold, and to to have someone include that in the in the logic is awesome because everyone knows if you're an operator in a warehouse, you know that's a factor. But if you're just looking at a spreadsheet, if you're a finance person looking at a spreadsheet, you would never think to include that in your calculation. But if you want to dial it in, it needs to be factored in. So I just thought that was insanely impressive that that would be included. And I think that that's a a hint at what else awaits in this type of, uh, program. So that's that was
Speaker 1
[00.09.46]
awesome. Thank you. You know, and obviously, you know, if you have any of your listeners have ever done in a multi tier facility, and you go up on the third level of your Pike tower in the summer. You can't even work out there. It's like torture. Um, but you still have to get the product out. So, uh, absolutely. Temperature band effects, fatigue rates and effects. Performance.
Speaker 2
[00.10.08]
Yep. Yeah. How many hours you've worked in the week? I mean, all these things begin to begin to come in there now. Now let me ask this question. So obviously when you're talking about supporting an operation, the the warehouse, the distribution piece of it in a lot of cases is what I call like the widget movers or the material handler people. But sometimes if you're selling something to an executive who is thinking about revenue and the money that is generated in the in the business, so do you ever have that, that gap, or have you noticed that chasm between the the sales side and the revenue and the executive side of the operation and then the, the, the warehouse side? And how have you gone about kind of being a liaison between those two because you it seems like a product like yours, you sort of having to serve both. It's providing value to both, but both sides of that seem to be speaking a little bit of a different language. Is that something that you've you've noted in, in your and your work? I it's it's the parent every single day. Um, you know, and again, if we go back to the term, the guys on the floor just need to get things done there for an ever putting out fires. Um, whereas the operations guys don't actually, you know, the the executives don't want to go in the warehouse because they might want to get their shoes dirty. But more importantly, they're dealing with the number side of things. Um, so if I'm an operator, uh, and typically operators throw as much labor as they can to get the job done, right? They're getting get things done, guys. Where the where the executives are. The guys saying, well, we want to get it done. Well, we also have to make a profit. Uh, you know, and in, in, in typical speak, uh, you know, the, the challenges, you actually have to equate money to touches for the people on the floor. And you have to equate touches to money for people upstairs. So you're absolutely every single day doing an interpretation of both.
Speaker 1
[00.12.14]
Yeah. Yeah.
Speaker 2
[00.12.15]
And I think he I think the more you do that and you and you've done this a long time, I think the more you you can be comfortable quickly stepping out of one and into the other. But it takes time to, to to do that. But I think once you once you get it dialed in, you know, the buzzwords to say, you know, the language to speak in, to really to really make it hit. So back to to labor AI, which just it's it's sounding to me like a very impressive tool. The more that the more that I learn about it. But what's what's the value prop? You know, I mean, when you go in and you're selling it, you're growing it. How are you making an impact for, for for both sides? I mean, is it purely a financial impact? Is it. Is it? Hey, my quality of life gets better and my operation, my morale goes up. You know, customer experience is is better in terms of order, turnaround time or really all the above. Like how do you what what do you do. What what do you sell this with when you go in.
Speaker 1
[00.13.15]
Well, there's two factors I think that we really focus in on here at labor AI. The first is actually rightsizing your building. So, you know, if you go to an operation where people are overtaxed, right? They're running hours and hours and hours of overtime. Um, you know, they get they get will call it, they get it burnt out. Um, and that onus falls not only on the clerical but on the people on the floor. Um, that's a problem inside of an operation. So understanding our workload, right. So on a day to day basis, uh, that's the key aspect of it. And of course, the warehouse manager who's tasked with putting out the fire, but then also doing the financial personnel. And they have no way of doing it because they're basically they don't they're too close to the fire to actually step back and go, oh, that person is not working or whatever. So that's part of the problem. So labor AI right sizes your labor. But then what we also do is because of we're a planning tool, right. Um, what we our software does is make some recommendations to you on non-capital changes in your business that you can make. That will derive and it will actually calculate what your savings will be. If you do this, this or this. So it's at the macro level. We're making recommendations on improving your operation or the design or process that you have. And secondly, we're actually managing out the labor. And the way the software sort of will save works is like an airplane. If your current productivity standard is 18, we'll call it 78% of utilization. That means your people are working. You're generating 78% of the time work, the software will be there. But then if you're at 78% for five days in a row, the software will then increase the standards to 79 and will help you raise gradually, incrementally where you're going. And if you get to a place where we call it stasis, where five days in a row we're at 81 and I haven't made that, it will drop the planning number back down to 80. And so what that does is tell you, oh, your KPIs at 78% are 12.1 pilots an hour on receiving and you can't get to 12.2 pilots an hour because you're traveling too far or whatever it is, or your rates are 160 cases an hour, 27 lines an hour, or whatever the numbers are. We want to get to a point where you can actually factor that in, as we continually rely on temporary labor. We have to then actually factor in that temporary labor is not as productive as an existing employee because they have to learn every day. So the software then factors in that, you know, maybe I'm starting at 71 because I'm always dealing with temporary labor. Right. So I might be lower productivity. And you know, the biggest problem for for an operator is that their warehouse is packed like they're at 98% of capacity. And because, you know, if they're three people, they're getting their storage money and their profit margin on profit on labor goes through the floor. Because as you get full, your productivity goes down. Yep. Right. It's it's rearranging the warehouse, rearranging your dishwasher so you can get into the dinner dishes, because I gotta move stuff around to free up space. So the software helps you look at all of this and plan for all of that.
Speaker 2
[00.16.46]
Yeah. And I think, like I've always said,
Speaker 1
[00.16.49]
that
Speaker 2
[00.16.52]
depending on the the facility that you work at or the company that you work for, there's a wide variation in how much visibility you will have on the metrics that you need to see. I mean, if you're at a big time fulfillment center, they have lots of data and it's easier for you to kind of manage your shift if you're running a traditional warehouse. Sometimes they just look at one PNL what was what was our labor spend? That's it. But I've always said, yeah, but you but you got an inbound piece, you got outbound fee she got picking there that labor is going into a lot of different places. So if you're only looking at it as one line on the PNL, you're you're really not doing yourself any favors. And let's say. Well, that's the only that's the only capability that we have. Kronos, our time keeping clock is not is not able to to to do it and everything. So it sounds like you're like you're you're in agreement with me there that a lot more that we measure. Right. The more these things that we have, the more that operators can, can dial it
Speaker 1
[00.17.48]
in. And and that's the whole thing about, you know, software solutions historically in the market for this have been extremely expensive to the point where the punitive or the not attainable. We built our model so that if you have 20 people or more in your warehouse, your ROI with our software is probably in the 10 to 50 X on our monthly suite. Um, so when our clients, whether they're global brands or small family shops, you know, the, the, the minimum that we're driving for a client on the we'll call it on the global stage, the biggest companies in the world. Um, you know, might be 3 to 4%. And on average we're getting, uh, 8 to 12% reduction in labor costs. Yeah, that's a big number for anybody. That's. Yeah, yeah. We have sites 20 people. We have sites with a thousand people. Yeah.
Speaker 2
[00.18.43]
And I know so and and um, like labor management systems are coming on the scene. Right. They're more readily available now. They're growing. There's lots of different LMS systems out there that you can have. And I think one of the reasons why. Is because people are realizing that if they have this visibility that, that they can really drive out the waste much better. And another thing I think that's happening is now there's so much cross-pollination in the distribution world. You have managers that kind of came up with a as an Amazon as an example, and then they go out to work at a family owned company that runs a warehouse. Well, they were used to having all of this type type data and information. Where's how do I have this ability. So we need we need this. But she gotta but an LMS is very expensive. I mean I mean sometimes up around 50 grand just to kind of get in the door so I don't, I don't I don't know if you want to talk about the, the price of, of your product, but I'm sure it's not $50,000. Uh,
Speaker 1
[00.19.47]
of what? Well, and again, true LMS again, the value of the LMS, um, is indicative of the cost. Uh, so in other words, yeah, 50 grand would be probably at the low end of the scale to get in. Yeah. Uh, and that's just the cost of the software. Then you have the services, because the LMS requires an engineer to go out on the floor and do a time and motion study for, oh, for the last six months, it's been 1.6 seconds to get on and off of the the the pallet jack or whatever. Yeah. Um, and so you're getting this derivatives and you have to continue to update your, your standards. And if I'm in a multi client facility for three people, I have to set standards for every single client. So it's not really applicable. Um, as you mentioned there are more coming on the market but they're more labor monitoring systems. They're using historical averages. They're not using what we call ILS or engineered labor. Yeah. Right. They're not using standards. They're just creating historical averages and saying, oh, you know, on average we do 12 pallets an hour. Great. So tomorrow we'll do 12 pilots an hour. But oh, you know, I've got ten different clients. Every client has a different model. Not saying that they don't have their place, because where the LMS is truly becoming a value in the marketplace is the onset of paying our employees sort of benefits on productivity. Yeah, right. So perform performance bonuses. Well, you have to have a measure of performance bonus after the fact. So you'll have the onset of orchestration systems that are basically looking at, oh, you know this this truck was late. So all the dependencies on that truck, I have to distribute that labor until that truck comes in. I don't want them all sitting there waiting like for the baby to come out. They have to do other tasks and orchestration takes that on as well. Labor AI currently is the only product that sits before the WMS, before the allocation, before it's moved into the system to tell you anything about your labor. That's the true value. And our cost for a single site, you know, runs between 7 and $800 a month. So if you've got a quarter million dollar spend and we're getting you, you know, ten, 12% at the low end, you know, it's not a great return. But you're talking about, you know, saving essentially, you know, 30, $40,000 a year for an $800 a year investment. That's at the small end. Our typical sweet spot for clients is between 40 and 500 users, and the model is still the same, so they pay $800. It's the same cost for a single single site customer, right? And they're still getting that 810%, but they're getting it now on 200 people. And so now now you're getting, you know, savings of five, $600,000. So our clients, because they range in size greatly and brands that across pretty much everything, every vertical market, um, we don't really have a challenge with regards to deploying for clients to make it simple for them to get their their value out of the software.
Speaker 2
[00.22.54]
I gotcha. Yeah. I mean, to me. I mean, it looks like a no brainer to me. I mean, I'm just learning about it and all things, but, uh, it seems like there's some insane value here for what you. What you pay. And, uh. Um. Yeah, it's very, very interesting. But let me ask this question, though. I mean, you know, we're talking about forecasting, right? So prescriptive analytics is, is the term that you use and we're forecasting. But how far out can you go. I mean is this is this the day before or a week before. A month before. How how how what type? Well,
Speaker 1
[00.23.27]
the answer to that is yes. Okay. Uh, so it's but forecasting is only as accurate as the forecast. Yeah. Um. Um, but basically our software you can forecast out. So again, depending on your business, uh, you can forecast out as far as you want. You can go out, you know, two weeks, 30 days. Um, we do some of our clients will do, uh, like a, you know, uh, shopping season forecast. So the last two weeks in November, including Black Friday, you know, they expect to do 12,000 orders. And, you know, so our software says, okay, if you're doing 12,000 orders, you have to make 12,000 boxes. You're fixing, you know, 2400 labels. And then we put on the work content. Oh, you need, you know, 37 hours a day or 3 to 700 hours a day on box building. You've got, you know, 12 seconds times, two times 3700, whatever your orders are for your boxes. It builds out all of these based on our table so you can forecast out accurately. Right. We're not. And this is the I guess, what companies in the industry really have an issue with is precision over accuracy. We're in in imprecise industry. I don't want to I don't want to do an advertisement. But there's a candy company out there that says, hey, every handful is an all new ball game, and every single day is different inside of our market. So we are not production. We don't control our own sort of entity, so we're always putting out fires. But if we can plan and get one step ahead, right, we don't want to be ten steps ahead of we're one step ahead. That's where we get to right now. Um, yeah. And then we can look at our trends and trends analysis and things like that. But you know, right now most companies are are still putting out fires every single day. Yeah. In the morning you go, oh, geez.
Speaker 2
[00.25.22]
Yep. And I and I've said that a lot. I mean, I, I appreciate the lean right. The lean is six Sigma crowd everything but that that has a manufacturing background to it and and it is standard work and there is a certain level of standard work that we want in distribution center. But there is a component of it that is non-standard every day, because you are constantly reacting to all the variables, and the variables are the customer, the the customer order rate, the challenges, the supply chain on the inbound piece versus the manufacturing. Sometimes where you pretty much know what you're being tasked to produce. When you when you come in and you and you go to that. So that makes it way more challenging. I think, uh, and warehouse managers like you said, have to be way more nimble and flexible and in dynamic. And that, that, that takes a toll on them. And so but like you said, if, if there's technology that can help them get a couple of steps closer, that's just, that's just going to go a mile. I mean, that's just going to go a long way. It's one of the biggest challenges, I think, in the industry is just that fluctuation in demand and just firefighting every day. And but you're always held to the standard of the service level agreement, the customer experience that at that part of the variable cannot change. Everything else has to change behind it. And um, yeah, any, any tools that can help leaders do that. I'm always all for
Speaker 1
[00.26.46]
them. Uh, and yeah, absolutely. You're there's no question that that is an issue in today's market is the variability in today's model. And even inside of the week, right inside of the month, we have, you know, you may have a, uh, you maybe a warehouse where you've got one day you're shipping toothpaste and there's 216 cases on a pallet. Um, and then the next day you're shipping, I don't know, laundry to soap, and you get 20 cases on a pallet. Well, the cost to to actually, if you're doing it at the case level, the cost of the laundry soap is five times more expensive to go through the warehouse than the toothpaste. But if I'm the executive. A case is a case. That's right. Right. Being able to determine that in advance is the true value of having a tool that can tell you what you need to do. So I can see that. Oh, if if I get notice from my customer or my marketing guys that we're doing a a promotion on laundry soap next week, I can run a model and figure out I'm going to be five people short. Do I want to have 40 hours of overtime? Or do I want to bring in four temps to do that? Or during the day? If I've got five businesses or five clients in my three PL, I would say, you know what, um, let's move four people from client one to client two to get rid of this and solve this problem. We call that labor balancing. Yeah. If I see that the day before, rather than at 12:00 in the afternoon on Friday, where I've got no options, I've got no options. Yeah. Hey, Ben, you want to stay for overtime? No. Not tonight. I'm going out with boys, you know? Yeah. You have. You're out of options.
Speaker 2
[00.28.26]
Well, and, I mean, I would I like to use a lot of car analogies, a lot like, because you mentioned the maps in the beginning. Hey, if I'm going on a trip. So then you will know ahead of time. I do not have enough fuel in my car to make this trip, or I do not have enough horsepower in my engine to drive at the sustained speeds that would be required to get there and when you want to get there. And so you would say, I need to do something about this. But in the warehousing world that just that doesn't happen, right? Like the sales happen and then the feedback to the manager is, what the hell happened? Why couldn't it why couldn't you do it? And I was I was five short. Well, no, I mean the math. Right. So I think like getting that, getting that fleshed out and getting that to the surface is going to help not only like for their mental wellbeing, but also just for the ops. Right. If everybody knows it's just a spade, a spade, take the emotion out of it. We ran the analytics. This is what it says. We need five extra people. If we don't have them, we will miss according to the analytics. And it doesn't matter what motivational speech you give me or what speech you give me as the manager. It's just facts, right? And here they are coming out of the system. That's why I, I love this because there's
Speaker 1
[00.29.39]
a, there's a, there's a lot of,
Speaker 2
[00.29.41]
there's a lot of perception. There's a lot of feelings. There's a lot of like anecdotal type things that go on in this industry and where people arrive at those conclusions, I don't know. Right. It's different for each person. So I love to get to the actual the data, the truth. And then you can you can go from there once you can get to the truth. So I love it.
Speaker 1
[00.30.01]
Part of my mind. My mantra has been for, you know, the last 30 years is let the data drive the solution. Yeah. Um, and it takes all of that, we'll call it, uh, noise out of the equation. Like if I'm comparing your site against my site and you come back and say, yeah, well, you get all the easy orders or, you know, you've got all the old senior guys or whatever it is, it doesn't matter. Uh, you take that noise out, you're buildings five times bigger than mine. Of course you're going to be more efficient. Right. Those are things where you take that out of the noise. And we do that for that specific reason. So you can compare productivity from one site to another, one customer, another across multiple metrics, multiple countries. Yeah. I love that. And it kind of it leads me into my next question or my next point. I guess it is. And that is um, and I've heard you talk about this before where executives, they always want to benchmark, right. They always want best practices. They want to know is my, uh, is my operation optimized or are we are we doing the best in class or are we doing the best practices? But is that I mean, I've always felt like maybe that's not the the best way to look at that. And you should look at what is the optimal for you and your facility and your setup with your product mix and your variables versus
Speaker 2
[00.31.27]
blanket, comparing them to what ten other facilities are doing, because there's so many variables and they're all different. And it is not always apples to apples. Is that far off. I mean, how do you how do you feel on that?
Speaker 1
[00.31.40]
Well, that's pretty pretty much bang on, right? Uh, so there's no such thing as best practices. Uh, there's optimal practices which are based on a your resources that are available, the size you're building, people. You have automation, your budget. Right. We all can't be Amazon. We all can't spend $50 million and still be inefficient, right? We can't be we can't do that. Um, and so then comparing one site to another, uh, by saying, oh, well, you know, I'm, I want to figure out who's the best in class for case picking. Right. And you look at okay, well, let's look at one of the grocery store chains because that's all they do. And they're built for all of that. But then I'm a e-commerce company that's picking socks out of a bin. I can't compare that. Um, yeah. So how do I and this is always the problem. I want to compare myself to my competition. But they're using different things. They use a different WMS or a different ERP. That's causing me all kinds of headaches. Like, these are things that, um, anybody that comes in and tells you all the best practices is doing this is really saying, oh, I've been into the the most expensive operations on the planet. I know how they work, and you should do that. You made the comment earlier about hiring an Amazon guy who goes into a different company and goes, oh, well, where's all your reporting engines and where's your exhortation system? Yeah, no, we can't afford that. Figure out the answer. Um, you know, to my problem, not to Amazon's problem. Yeah,
Speaker 2
[00.33.11]
absolutely, absolutely. And because I and I've said this before, too, like, I feel as if oftentimes you
Speaker 1
[00.33.18]
could, you could
Speaker 2
[00.33.20]
go to a quote unquote underperforming facility. I've seen this happen where you go to a facility they're underperforming here. You could take if you could lift those people out of that facility, include the managers and drop them into a facility that is highly mechanized, is is automated, has technology. They would then become a high performing staff. And if you if you swap if you vice versa if you took the people from that facility and dump them into this warehouse with no tech chock full of goods packed to the brim, they would quickly become an underperforming. Right. And so it's not always necessarily due to an underperformance of the people. A lot of times it's just the tools that they have or don't have, the resources that they have or don't have, and the unique properties and variables that are applicable to to them where they work. Right. And when you get all those things out, I think you get to that optimal number optimally based on all that. This is how we should be performing now if we're severely underperforming now. Okay, right. But to hold us to the same standard that an Amazon FC is running at and we are a mom and pop three people or a warehouse is
Speaker 1
[00.34.34]
just it's just
Speaker 2
[00.34.36]
foolish really, in my opinion. So what? And
Speaker 1
[00.34.40]
this is the difference between your operator and your executive because your executive thinks we should be Amazon. Uh, and I'm measuring I'm not measuring performance of my people. I'm measuring performance of the operation on throughput. Right. So activity. Okay. Theoretically what you said is true, but in actual fact, the more automation you have, the less productive your people are. Because the automation masks or hides their lack of productivity. They let the machines do it, and they stand around and do a lot of watching of the automation. Whereas in a non mechanized operation, people do tasks that there's no metrics for, right? So they're writing their handwriting down, you know, your orders on a piece of paper rather than scanning an RF. Well okay. Great. Did you accommodate in your plan for the 34 seconds? It is for the no. So that just goes is underutilized time that oh well, they're not performing. So the more people travel and they're walking, in fact, their productivity rate goes higher because when I'm walking and I'm calibrating you walking, you're actually working, right? I might I not be producing, but I'm actually I'm working at as fast as I can. You can't ask somebody to walk at seven miles an hour, right. So yeah, you can't and you can't ask, you know, somebody to be picking a 42 box of, uh, break carts at 35 an hour, you know, you'll have nobody left. They'll have no arms. Right? So these things masks the automation, masks the productivity. And I think what happens is people get depressed because they think I should be doing the same work where that guy over there, he's in the automated operation or the automated section. He's pressing buttons and I'm lifting brake parts. Yeah. How demotivating is that?
Speaker 2
[00.36.29]
Yeah, it's like we're gonna have. We're gonna have a race. Li I am going to drive a Mercedes, and it is going to be very smooth and very fast. And you are going to be in a manual Jeep Wrangler with
Speaker 1
[00.36.43]
no top on it. No, no, I'm in a potato sack potato
Speaker 2
[00.36.47]
a potato sack.
Speaker 1
[00.36.50]
Yeah.
Speaker 2
[00.36.50]
Like I went to race and in the Mercedes. But like who actually performed better. Probably you in the in the potato sack or from an
Speaker 1
[00.36.59]
experimentation from. Yeah, absolutely. And so this is you know, this is the difference between how management looks at things. Thinks that everybody should be in a Mercedes. But the reality is that my people are working like crazy in a potato sack. But we're not factoring in all the all the touches and all the things that they're not doing. So we try and highlight that as underutilized time. Um, typically underutilized time. Is a function of floor level management. It's not the people. Yeah. Yeah. So if you know part of and again we'll probably talk on this briefly, but I'm, I'm basically came up with a theory called the the labor paradigm. And essentially what it means is, you know, you go out on the floor, you look on the floor on Mondays you do 5000 cases, then on Wednesdays we do 8000 cases, and on Friday we do 3000 cases. But I'm the manager. I look out my window every day and everybody's working, right. And it doesn't matter where you are in the world. This happens on Mondays. Guys walk this fast because they know it's an average day. On Wednesday. They walk a little bit faster. Why? Because they don't want to work overtime, right? And on Friday they don't want to get sent home. So they walk slower. And this is this collusion. And if I don't have standards it's just people work to the activity. Right. So they they mirror each other. So if I know it's a slow day I'm going to slow. If it's a fast day I'll work fast. Operations don't have the ability to look at that. It's it's it. They don't see it. You know, if you can identify that and highlight that on a day to day basis, you will win the game.
Speaker 2
[00.38.37]
Yeah. And I think those are
Speaker 1
[00.38.39]
just there's a term that I like that that is growing. And that's the term operational reality. Those that is an operational reality of the world that we live in. People humans incentives how they whatever. And so if you are a 20 year veteran manager, you can probably pick up on a lot of those things because you've learned an experience. If you're a new manager in the industry, you're not going to you
Speaker 2
[00.39.07]
have you have no hope. You don't know those things, right? So if there's no tool or technology that can kind of help guide you in that, your your tribal knowledge, your experience is not going to be enough because you you just you don't have it. You don't
Speaker 1
[00.39.19]
have it. Well, they don't have. Right? Yeah. They don't have the experience. Right. The average manager has less than six years warehouse experience total. Yep. No. And it takes a it takes a while, right. It takes it takes a it takes a while to to learn and pick up on on all those things. So, so let me let me do this. I think you I think you're hinting at me that you want to, uh, talk about your book. So you have written a book, which is awesome, by the way, called warehousing 101, uh, reference guide. Um, which is
Speaker 2
[00.39.50]
great. Um, I love it. I, I, I appreciate all the thought that went into this, but can you tell us about your book? You know what? Like what? What motivated you to write it? What was it like writing it? And you know, what types of things have you have you poured into it out there for the community to benefit from?
Speaker 1
[00.40.08]
So in probably a we'll call it a self fulfilling model. I, I'm tired or I was tired of teaching companies about warehousing. Um and so it made it I and as I've been going through the market I've seen lack and and we'll call it the knowledge base and in supply chain is dropping every single year. Right. And the new generation that's coming in like they don't understand the terms. They don't understand like, you know, the basis of designing a building or whatever. And and so all of the stuff that I've been creating for 20 years to do training sessions and things like that, um, I decided that I would put it in one place where I could say, okay, rather than me coming in and teaching your people, here's a book, here's a read the book. You can learn about the types of travel you can learn how to slot. You don't need me to come in and do it. And at the same time, I'm passing on the knowledge to the next generation, because I don't think people understand the gap. And the problem that we're going to get to is if the next generation doesn't know what they're doing, we don't have the time. Or that will call it the gap to make the mistakes that we did in the early 2000. Right. You used to have an order, you know, turn a cycle. Time is 72 hours, three days. Now you're lucky if you get, you know, 12 hours. So I make a mistake. That mistake over 12 hours now could be 100,000, $300,000, because you just don't know what you're doing. So putting in the book the ability. Recording, uh, bringing new managers, bringing in people that want to get into the industry. There is no definitive document anywhere that talks about, oh, what are the basics of warehousing? You know, what is slotting? Why do I plan for labor? What types of travel, how does travel accommodate in my business? If you go to people in the industry and ask what's what? If I told you about 100% of my labor, how much is travel? What percentage of my 100% is travel okay? People don't understand that it's somewhere between 50 and 60% of my employees. Time is travel. If you don't understand the basics of that or the basic rules of thumb about, oh, if that's, you know, 50% of my travel is that and 50% of my labor is in picking. That means that 25% of the time that people are traveling and picking, they're doing nothing but walking or traveling. If I can reduce that or make that faster, that's a benefit to us. I go through around the globe, and that concept is foreign to probably 80% of the companies that I talked to. So I wrote the book so that whether you're just getting into the industry, you want to start getting into consulting, you want to start operating on your own business. This allows you to do that, and it gives you a frame of reference. There's also a glossary of terms in there that, you know, if you're coming into the industry, people in in warehousing actually know this. We don't talk in real language. We talk in acronyms. And and and if you don't have a source to go find those acronyms, it's it's brutal. It takes you years to learn and understand what we're doing. So the idea is to build the science in a book, in a reference manual where you can go in and say, oh geez, what is, you know, slotting and read up on it or whatever, you know, how do I slot, you know, why or why do we slot and all those things? Or why do we labor plan? Um, and so that's why I wrote the book. It was self-serving, so I didn't I was getting tired of going and traveling and and teaching companies about warehousing. And these are the professional companies. Yeah. They don't have an onboarding process to bring you into the industry. So that's why I wrote the book.
Speaker 2
[00.44.04]
Yeah, I like that. I mean, that's one of the reasons why I started the website, because this industry is so whack a mole, right? It can be all over the map. It's just figuring out what's what and how you learn this and who you got this from. It's all piecemeal, right? There's not very many centralized documents or centralized places that you can go to learn how to execute and do well in this industry. Each kind of company has their own little thing, but it's very har scratching surface, right? It's high level. And so I just think this industry in general can can utilize things like this much more, uh, much more resources, much more networking to, to gain this
Speaker 1
[00.44.45]
knowledge
Speaker 2
[00.44.46]
faster than learning it the hard way. After a ten year career or a 15 career, 15 year career. And by that point, you're you're kind of cooked, right. Like, you got kind of burnt out. I wish I would have known this. And your number two or your number three, uh, versus having to learn it in 6 or 7. So I, I, uh, I appreciate that. Um, I really like that you wrote that book, and I'm going to be picking it up. I'm going to be picking that up because it seems to be very interesting to me. So. All right. Lee. Well, so where can people find out more about you, uh, and your organization? I know I've seen I've seen the brochure. I know you're on LinkedIn, I know you, I know you're traveling. If people want to work with you or or or labor or labor AI, uh, where do they go? How do they find you?
Speaker 1
[00.45.33]
Uh, you can go to labor ai.com. Uh, that's our our website. Obviously, you can connect with me through there. Um, happy to answer any questions you can get me on LinkedIn. I'm all over that. Um, you know, for those of you that are sort of in the outskirts of, uh, you know, cities and things like that, um, you can see me on a milk carton. Uh, so, you know, I'm basically available pretty much anywhere. And then we have, uh, we have offices in Bangkok, Sydney, Australia. We've got, uh, people in Europe, uh, Toronto. Florida. San Francisco. So we're awesome. We're growing rapidly and, uh, always looking for new people, for our organization and for new customers.
Speaker 2
[00.46.19]
Awesome. Uh, I love that, uh, I, I've really enjoyed chatting with you today. I really mean, I mean, I say this to every guest that comes on the show, but I really have enjoyed chatting with you because I, I, I appreciate your insightful and I, I really like I really like the, the solution that you've created, uh, because I think it just solves a lot of pain points and gets things to the data, which is where they need to be in this industry. Uh, and so I, I will always champion people that are doing both of those things, helping warehouse managers, warehouse leaders, and moving the ball forward in terms of tech to get the data fleshed out. Because that's where the truth is. Once you get there, then you can really do some things that are that are that are right. Get rid of those biases and those perceptions. So, uh, I really appreciate you coming on the show, man. Uh, I, uh, I'm sure I'll see you around the LinkedIn streets, as they say. I live out there. So, uh, uh, thank thank you again, my friend. It was, uh, it was an honor and pleasure. And best of luck to you and labor AI in the future.
Speaker 1
[00.47.19]
I appreciate all your time, Ben. Thank you so much.
Speaker 2
[00.47.22]
Yeah, you got it, Lee. Thank
Speaker 1
[00.47.24]
you.
Speaker 2
[00.47.25]
All right, guys. Well, that concludes today's show. Thank you again to Lee Richter, the CEO of labor AI, for coming on to talk about the solutions that they are providing out there in the industry, the resources that they're bringing. It sounds very cool, very unique and very needed out there in the industry. So check him out. Check out his book warehousing 101 A Reference Guide. Very cool piece of literature there, and I appreciate what he is doing with handing that out to leaders across the globe to really teach them about warehousing. As the Warehouse Underground goes. Please sign up for the community if you haven't done that. I think we almost have 200 people that have signed up for the community. We're approaching that networking in there, some message boards, and we have some very cool live events that I'm going to be revealing pretty soon that are coming down the pipe that you guys won't want to miss and that you will want to be a part of. If you have not checked out the Tiger team, look for episode six coming out next month with some wit, humor and sarcasm. All things going on out there in the world of warehousing and distribution. So as today goes, thank you again for Lee. I will see you next week for episode number 25. Thanks guys. God bless.
