Meeting Title: Chuck <> Brainforge - Shipping-Weekly-Meeting Date: 2024-07-11 Meeting participants: Jakob Kagel, Nicolas Sucari
WEBVTT
1 00:01:40.260 ⇒ 00:01:41.130 Nicolas Sucari: Hi Jakey.
2 00:01:43.730 ⇒ 00:01:44.869 Jakob Kagel: Hey? How’s it going.
3 00:01:46.550 ⇒ 00:01:48.649 Nicolas Sucari: All good here. How about you?
4 00:01:48.980 ⇒ 00:01:50.852 Jakob Kagel: Doing well doing? Well.
5 00:01:52.810 ⇒ 00:01:56.650 Jakob Kagel: cool. Yeah. I guess just we’ll give a couple of minutes for Chuck to join.
6 00:01:57.360 ⇒ 00:01:58.050 Nicolas Sucari: Yeah.
7 00:01:59.870 ⇒ 00:02:02.570 Jakob Kagel: You’ve been watching Copa. America had the fun.
8 00:02:02.979 ⇒ 00:02:03.389 Nicolas Sucari: Yes.
9 00:02:03.690 ⇒ 00:02:04.440 Jakob Kagel: Yeah.
10 00:02:04.900 ⇒ 00:02:05.680 Nicolas Sucari: Yeah.
11 00:02:06.470 ⇒ 00:02:09.820 Jakob Kagel: It’s gonna be a good one. That’s gonna be tough for sure.
12 00:02:10.100 ⇒ 00:02:12.780 Nicolas Sucari: It’s gonna it’s gonna be super tough. Colombia is doing.
13 00:02:12.970 ⇒ 00:02:19.712 Nicolas Sucari: It’s doing fine, and they’re playing. Well, I I was hoping it was Colombia. Not do I window? Add.
14 00:02:20.050 ⇒ 00:02:22.790 Jakob Kagel: You think otherwise? More challenging kind of.
15 00:02:22.790 ⇒ 00:02:29.730 Nicolas Sucari: Urugu, Uruguay for us is kind of more tougher. I think there is kind of some like
16 00:02:29.990 ⇒ 00:02:32.509 Nicolas Sucari: rivalry between Argentina and Uruguay.
17 00:02:32.700 ⇒ 00:02:38.719 Nicolas Sucari: and it makes it. Oh, it’s always makes it difficult. But Colombia is playing well. So
18 00:02:38.850 ⇒ 00:02:39.899 Nicolas Sucari: yeah, I don’t know.
19 00:02:40.120 ⇒ 00:02:53.220 Jakob Kagel: Yeah, I mean they they they squeaked it out after the guy got sent off. Okay, I was like, you can’t do that like you come, maybe you can.
20 00:02:53.220 ⇒ 00:02:53.940 Nicolas Sucari: Yeah.
21 00:02:53.940 ⇒ 00:02:56.930 Jakob Kagel: Go push them off of you or something, but you can’t just.
22 00:02:57.180 ⇒ 00:03:01.013 Nicolas Sucari: These modern days with the video rep, and that
23 00:03:01.850 ⇒ 00:03:02.519 Nicolas Sucari: do that shit.
24 00:03:02.520 ⇒ 00:03:08.583 Jakob Kagel: Yeah, you’re gonna watch it a hundred times the whole stadium is, gonna see it? But
25 00:03:09.100 ⇒ 00:03:14.210 Jakob Kagel: yeah, it was a. It was a good game, nonetheless. So yeah.
26 00:03:14.810 ⇒ 00:03:22.473 Jakob Kagel: I don’t know. I don’t want either Spain or England to win in the euros. So I’m just, I’m gonna watch.
27 00:03:23.170 ⇒ 00:03:25.590 Nicolas Sucari: We’re cheering for Germany right?
28 00:03:25.590 ⇒ 00:03:27.326 Jakob Kagel: Yeah, yeah, exactly. So
29 00:03:28.160 ⇒ 00:03:31.868 Jakob Kagel: I’m like, either way it goes. It’s it’s bad.
30 00:03:32.980 ⇒ 00:03:40.470 Jakob Kagel: But we’ll see. I guess if Spain wins. Germany’s maybe vindicated a little bit, because they lost to the champions, you know, but
31 00:03:40.960 ⇒ 00:03:41.530 Jakob Kagel: still.
32 00:03:41.530 ⇒ 00:03:42.360 Nicolas Sucari: Yeah.
33 00:03:42.360 ⇒ 00:03:43.169 Jakob Kagel: Like strange.
34 00:03:43.170 ⇒ 00:03:44.830 Nicolas Sucari: Spain is playing well.
35 00:03:45.020 ⇒ 00:03:49.340 Nicolas Sucari: and but England probably is not playing so well that they win.
36 00:03:49.791 ⇒ 00:03:56.570 Jakob Kagel: They they every game they score like in the last minute, like, you know, they.
37 00:03:56.570 ⇒ 00:03:58.599 Nicolas Sucari: Yeah, they turn around all of the magic.
38 00:03:58.600 ⇒ 00:03:59.405 Jakob Kagel: Thanks.
39 00:04:00.628 ⇒ 00:04:03.900 Jakob Kagel: No, I I’m tired of them, too, but
40 00:04:04.170 ⇒ 00:04:06.947 Jakob Kagel: it’s all good. It’s all good.
41 00:04:07.410 ⇒ 00:04:17.529 Nicolas Sucari: It’s it would be interesting if if Argentina wins on Sunday, and if England wins also on Sunday, they will be playing
42 00:04:18.070 ⇒ 00:04:24.740 Nicolas Sucari: like next year. I think it’s like kind of the penalty call, or something like that.
43 00:04:25.400 ⇒ 00:04:26.030 Jakob Kagel: Right, right.
44 00:04:26.030 ⇒ 00:04:29.999 Nicolas Sucari: Where? Where the 2 champions of the of America and the champion of the
45 00:04:30.600 ⇒ 00:04:32.670 Nicolas Sucari: euro with a
46 00:04:32.730 ⇒ 00:04:35.590 Nicolas Sucari: they play against. So it will be interesting.
47 00:04:35.590 ⇒ 00:04:36.909 Jakob Kagel: Did I tell you?
48 00:04:37.590 ⇒ 00:04:40.570 Jakob Kagel: I went to the Copa America game in Q 2
49 00:04:41.047 ⇒ 00:04:43.829 Jakob Kagel: like Costa Rica. Paraguay is in Austin.
50 00:04:43.830 ⇒ 00:04:44.430 Nicolas Sucari: Then.
51 00:04:44.430 ⇒ 00:04:45.470 Jakob Kagel: I’m not. Yeah.
52 00:04:46.070 ⇒ 00:04:58.691 Jakob Kagel: Yeah. It was cool. Yeah, it was like, I think I was maybe saying already. But like Costa Rica, they had to make up like a 6 goal difference twice like in the 1st 7 min I was like, Oh, my God, they’re gonna do it.
53 00:04:59.910 ⇒ 00:05:04.358 Jakob Kagel: Didn’t happen. It’s like they’re gonna get 5 goals in this game, and I don’t know
54 00:05:05.150 ⇒ 00:05:06.740 Jakob Kagel: but we’ll see.
55 00:05:08.330 ⇒ 00:05:11.710 Nicolas Sucari: Yeah, in the in 2 years
56 00:05:11.760 ⇒ 00:05:16.020 Nicolas Sucari: you’ll be having the World Cup here. So that will be interesting.
57 00:05:16.380 ⇒ 00:05:17.594 Jakob Kagel: Right? I mean,
58 00:05:21.581 ⇒ 00:05:23.468 Jakob Kagel: Yeah, that’ll be cool.
59 00:05:25.380 ⇒ 00:05:29.270 Jakob Kagel: for sure. I mean, they definitely have some games in Texas, I think. Yeah.
60 00:05:29.850 ⇒ 00:05:31.800 Nicolas Sucari: He’s gonna be different
61 00:05:32.222 ⇒ 00:05:36.320 Nicolas Sucari: than the past world cups. But yeah, it’s gonna be interesting.
62 00:05:37.160 ⇒ 00:05:39.609 Nicolas Sucari: You want me to ping. Okay, you’re writing.
63 00:05:39.610 ⇒ 00:05:42.937 Jakob Kagel: Yeah, I was already gonna send it. But yeah, I think
64 00:05:43.920 ⇒ 00:05:44.906 Jakob Kagel: was the
65 00:05:46.060 ⇒ 00:05:52.980 Jakob Kagel: I mean, I can just talk a little bit, too, I guess, like before he comes, I guess, about like what I’m seeing. So
66 00:05:53.190 ⇒ 00:06:03.817 Jakob Kagel: basically, like, put right like the zip codes for the 3 warehouses, and then calculated the zone like for every shipment. Right?
67 00:06:04.530 ⇒ 00:06:05.730 Jakob Kagel: so
68 00:06:06.560 ⇒ 00:06:11.530 Jakob Kagel: basically like when we look at like the discrepancy of the zones like which ones like
69 00:06:11.730 ⇒ 00:06:30.826 Jakob Kagel: shipped from New York. Cause may basically everything shipped out of New York. I mean, we don’t actually have, like California or Florida shipments yet. But when we look at like, okay, what ship from New York that could have shipped out of like a different location like either Florida or California. The biggest chunk is orders to Texas.
70 00:06:31.150 ⇒ 00:06:31.689 Nicolas Sucari: Right, so.
71 00:06:31.690 ⇒ 00:06:49.810 Jakob Kagel: That is kind of confirming it’d be. It’s like New York Zone 7, basically. And it’d be Florida Zone 5, and when we break it down, even like more kind of like to the city level. And see like, okay, which cities are like have like sort of this discrepancy in shipping zones.
72 00:06:49.870 ⇒ 00:07:13.127 Jakob Kagel: It’s like more on the East side, like there’s not a lot of California to tech to like West Texas orders. It’s just not doesn’t have the same kind of volume. Where it’s like the ideal zone is California. Because I’ve made like another column to that. Just says, like, you know we have, like the 3 zones. But then we say, Okay, which one is the the shortest like which one is the lowest
73 00:07:13.830 ⇒ 00:07:14.980 Jakob Kagel: So
74 00:07:15.380 ⇒ 00:07:34.339 Jakob Kagel: for the cities. It’s like Houston. It’s like Katie is like a suburb of Houston. It’s it’s a lot more like on the eastern side of Texas. And then it’s also like when we look to it like sort of this, like shipping discrepancies. It’s a little bit more like 2 in these
75 00:07:34.500 ⇒ 00:07:36.010 Jakob Kagel: States that are
76 00:07:36.150 ⇒ 00:07:48.137 Jakob Kagel: nearby, like Illinois, North Carolina, and Tennessee, they have like the same. They’re basically like the way point, like they have the same. They’re like New York zone 4, but then they’re like Florida zone 4
77 00:07:49.010 ⇒ 00:07:56.950 Jakob Kagel: but I mean there’s still opportunity there, you know. If you open a new warehouse to make it zone 2 or zone, 3
78 00:07:57.190 ⇒ 00:08:17.080 Jakob Kagel: or zone one, I guess. I don’t know but those are, I mean, that’s kind of to like when we look at like the center of gravity analysis. And it’s true, like the way that we calculate it this way doesn’t account like for our existing warehouses. The point that it dropped it at is basically in the very like west part of Tennessee. So
79 00:08:17.630 ⇒ 00:08:18.840 Jakob Kagel: it’s like.
80 00:08:19.440 ⇒ 00:08:21.880 Jakob Kagel: it makes sense to me, based on like.
81 00:08:22.169 ⇒ 00:08:22.459 Nicolas Sucari: Great.
82 00:08:22.460 ⇒ 00:08:33.378 Jakob Kagel: That we’re seeing, too, that, like the location, you know, should be more closer, like sort of to the eastern part of Texas. I mean, I’m not saying has to be in Tennessee, but.
83 00:08:33.700 ⇒ 00:08:39.360 Nicolas Sucari: No, no, I know also, I think we would, what we’ll finally determine where we are. Gonna
84 00:08:41.010 ⇒ 00:08:45.229 Nicolas Sucari: Use these new warehouses, the locations that units has available right?
85 00:08:45.230 ⇒ 00:08:55.082 Jakob Kagel: Right, exactly, and that’s what I’ve talked to Tom about this already. But it would be really good for us to get like a short list, or some kind of like
86 00:08:55.690 ⇒ 00:09:02.870 Jakob Kagel: you know, kind of list of warehouses that are like in that area. I mean, I have no idea how many warehouses they have. If this is like.
87 00:09:02.870 ⇒ 00:09:04.859 Nicolas Sucari: Yeah, yeah, probably we can ask them, yeah.
88 00:09:05.350 ⇒ 00:09:07.800 Jakob Kagel: Or something I mean, like.
89 00:09:08.030 ⇒ 00:09:35.030 Jakob Kagel: you know, a hundred is like, doable, basically like, you know, I mean, if it gets to be like a couple of 100. It’s like, okay, that’s a lot we need to like short list it somehow like, reduce the that we’re looking at. But if we can just get basically the zip codes of the warehouse. Then we can see sort of like, which would be more ideal. And that’s the next step for us, I mean, we’ll also, of course, like the center of gravity, like
90 00:09:35.070 ⇒ 00:09:52.689 Jakob Kagel: with it. That’s like a little bit more complex like with the clusters. Like, he was saying, that will help account for like our existing warehouse locations. But I think, really like, what is yeah, really good is like, exactly. If we can just get a short list of what warehouses they actually have.
91 00:09:52.690 ⇒ 00:09:53.670 Nicolas Sucari: Yeah, yeah.
92 00:09:53.670 ⇒ 00:09:54.950 Jakob Kagel: Voices are.
93 00:09:55.290 ⇒ 00:09:57.439 Nicolas Sucari: And if we, if we have that
94 00:09:57.660 ⇒ 00:10:09.279 Nicolas Sucari: like, if we have those that list, and we choose like 2 or 3 different ones, is it possible to to like. Take one example, one as an example, and just say, like, How
95 00:10:09.470 ⇒ 00:10:25.410 Nicolas Sucari: ha! Like, how did do the percentage of shipping zones change like from zones one to 8, using one or another like to compare how we could can change that percentage of all orders, shipments.
96 00:10:26.000 ⇒ 00:10:29.569 Jakob Kagel: Yeah, I mean, we can’t say, of course, exactly like, how will change.
97 00:10:29.570 ⇒ 00:10:30.380 Nicolas Sucari: Cozy.
98 00:10:30.380 ⇒ 00:10:43.519 Jakob Kagel: Right. But what we can say is like, if this like, if we, if we do, all the shipments based on the ideal zone, like the shortest. Right? Then, yeah, we can say like this percentage, would, you know, come from this warehouse?
99 00:10:44.087 ⇒ 00:10:54.409 Jakob Kagel: Like. And and compared to the other 3, because we can do the exact same thing that that I just did right. It’s like we have the zone for every warehouse, for every order. So it’s like
100 00:10:54.480 ⇒ 00:11:06.349 Jakob Kagel: when we do that, it’s like, then we just have to create a flag that says, like, Okay, this is the ideal warehouse based. And then we can split up the order volume like by that. Exactly so.
101 00:11:06.350 ⇒ 00:11:11.789 Nicolas Sucari: Yeah, I think that that is great, and that is what we need to do in order like to
102 00:11:12.396 ⇒ 00:11:15.029 Nicolas Sucari: to to to make them
103 00:11:15.839 ⇒ 00:11:29.510 Nicolas Sucari: make the right decision right like. If if we, if we show them how the distribution of phones will change, if we add a warehouse in Texas, or in other place like, how much can they earn
104 00:11:29.870 ⇒ 00:11:31.149 Jakob Kagel: So reducing? The yeah.
105 00:11:31.150 ⇒ 00:11:32.709 Nicolas Sucari: Decent. Level. Yeah.
106 00:11:32.710 ⇒ 00:11:39.780 Jakob Kagel: Cause there’s still like there’s a lot of orders that ship from New York to Florida, or that ship to Florida, but that was also because we didn’t have a warehouse in Florida.
107 00:11:39.780 ⇒ 00:11:40.410 Nicolas Sucari: Yeah.
108 00:11:40.410 ⇒ 00:11:45.530 Jakob Kagel: But it’s like I’m not like, I’m not considering those like I’m already like, I mean, I don’t know. It’s obviously.
109 00:11:45.530 ⇒ 00:11:47.720 Nicolas Sucari: Yeah, but that I think that assumptions are okay.
110 00:11:47.998 ⇒ 00:11:54.690 Jakob Kagel: Yeah, I don’t think it’s like a hundred percent right that, like every order in Florida, will ship from Florida warehouse or something, but
111 00:11:54.880 ⇒ 00:12:01.454 Jakob Kagel: I think it it should be safe to assume, like 90% or something like, you know, like.
112 00:12:01.820 ⇒ 00:12:02.689 Nicolas Sucari: We need to.
113 00:12:02.690 ⇒ 00:12:12.887 Jakob Kagel: Same with when you look at when you look at like the ones like in what I’ve done like so far. And Rebrand here, I’ll just share my screen so we can just look at it real quick.
114 00:12:13.410 ⇒ 00:12:21.539 Nicolas Sucari: What what we need to ask Jack, is is if every order can be shipped from any warehouse, or there are specific warehouse to ship
115 00:12:21.580 ⇒ 00:12:24.290 Nicolas Sucari: any specific order, item, or.
116 00:12:24.579 ⇒ 00:12:24.869 Jakob Kagel: Stuff
117 00:12:25.000 ⇒ 00:12:25.929 Jakob Kagel: that because.
118 00:12:25.930 ⇒ 00:12:30.130 Nicolas Sucari: That will make us change a little bit. The analysis, I think.
119 00:12:30.710 ⇒ 00:12:33.769 Jakob Kagel: Right? So you can see my screen right.
120 00:12:34.230 ⇒ 00:12:34.800 Nicolas Sucari: Yeah.
121 00:12:35.140 ⇒ 00:12:41.649 Jakob Kagel: Okay, sorry. This is a little bit big, but you can see here, like on the far right, like here we have, like the ideal zone, right.
122 00:12:41.650 ⇒ 00:12:42.660 Nicolas Sucari: Yeah, perfect.
123 00:12:42.660 ⇒ 00:13:03.980 Jakob Kagel: Like we can filter this like to the warehouse right? So most of them are going to be New York. I mean, there are some Florida orders, but it’s not enough volume really to like for to say that it’s like significant. So I think right now it’s just filtered to Florida or no yet. New York to but we don’t care about New York, really, because we already know, like this stuff.
124 00:13:06.560 ⇒ 00:13:13.298 Jakob Kagel: but yeah, it’s like, you can just see, like, based on the order volume. It’s like all the top ones are like Florida, basically
125 00:13:14.740 ⇒ 00:13:16.059 Nicolas Sucari: Yeah, yeah, that’s fine.
126 00:13:16.320 ⇒ 00:13:22.879 Nicolas Sucari: Yeah. And why? Why? Someone said, why, there are some multiple zones. What does that mean?
127 00:13:22.880 ⇒ 00:13:26.870 Jakob Kagel: Well, zone just means like, okay, this is New York Zone 4 and Florida zone 4. So.
128 00:13:27.250 ⇒ 00:13:31.050 Nicolas Sucari: Okay, okay, okay, it depends on shipment cost. Yeah.
129 00:13:31.050 ⇒ 00:13:34.389 Jakob Kagel: But but it I mean, yeah, exactly. I mean.
130 00:13:34.620 ⇒ 00:13:40.279 Jakob Kagel: I didn’t go deep edge deep into it to like, you know, really try to break down, you know, if it’s cheaper.
131 00:13:40.280 ⇒ 00:13:42.180 Nicolas Sucari: No, no, that’s fine. That’s fine. Yeah.
132 00:13:42.180 ⇒ 00:14:10.379 Jakob Kagel: This is just like from the multiple zones. And I mean, we have, like, yeah, all the splits here to like where this one’s just filtered like on Texas like right now. But we can see like for every like shipping provider, right like what order price per pound is. And then, I think, like skew and product class, I mean, this is like, basically what we’re saying before. It’s like most the majority of the orders here, like in Texas are all like brushes.
133 00:14:10.700 ⇒ 00:14:11.310 Nicolas Sucari: Yeah, but.
134 00:14:11.310 ⇒ 00:14:15.224 Jakob Kagel: Are like a couple of pumps like that I highlighted here.
135 00:14:16.090 ⇒ 00:14:21.590 Jakob Kagel: I don’t know. I mean, the brushes are just so much more expensive to ship. It’s kind of crazy, I mean.
136 00:14:21.730 ⇒ 00:14:23.190 Jakob Kagel: I would say, like.
137 00:14:24.020 ⇒ 00:14:25.940 Jakob Kagel: I don’t know like just.
138 00:14:25.940 ⇒ 00:14:30.229 Nicolas Sucari: So when you say expensive is comparing, comparing price per pound.
139 00:14:30.230 ⇒ 00:14:40.120 Jakob Kagel: Right price per pound. I mean, like, this is like, okay, this brush got ordered 600 times, and it costs like $13, like per pound, basically to ship, like, you know,
140 00:14:40.420 ⇒ 00:14:40.970 Nicolas Sucari: There!
141 00:14:41.720 ⇒ 00:14:49.039 Jakob Kagel: I don’t know. I mean, I also have like some sort of doubts about our data, too. I mean, I don’t know.
142 00:14:49.600 ⇒ 00:14:53.899 Jakob Kagel: You know I can’t really go through and validate everything every time. But.
143 00:14:53.900 ⇒ 00:14:55.030 Nicolas Sucari: Yeah, yeah, I know.
144 00:14:55.030 ⇒ 00:14:57.972 Jakob Kagel: I don’t know about the shipping weights like
145 00:14:58.660 ⇒ 00:15:00.190 Jakob Kagel: I don’t know. But
146 00:15:00.440 ⇒ 00:15:15.429 Jakob Kagel: anyway, I mean it. It doesn’t seem that far off, I mean. But it’s like the pumps and stuff are all basically like, you know, one like, you know, a dollar per pound or something which is like if the pump is like 20 pounds or something. I mean, that’s like
147 00:15:15.570 ⇒ 00:15:31.220 Jakob Kagel: makes sense, you know, and it’s like the price per pound. It’s like, Okay, it’s maybe 13. You know the shipping cost might be like $7, and the thing is like half a pound, you know, for the or whatever. So
148 00:15:31.250 ⇒ 00:15:32.380 Jakob Kagel: I mean.
149 00:15:32.840 ⇒ 00:15:56.237 Jakob Kagel: I don’t know. That’s like, but that’s why I also have, like the the average order like price per order, like for shipping. This is like how much it cost to ship each order. And that’s where you can see? Like, okay, the pumps are like cost, like maybe $40 to ship or something here, but it’s only like a dollar per pound, you know, cause they’re like, I don’t know. They weigh 30 pounds or whatever
150 00:15:57.400 ⇒ 00:16:06.070 Jakob Kagel: but that’s something like, yeah, we can discuss like more with them, too. I think. Yeah. Like the like. You said. I think the big thing is like.
151 00:16:07.010 ⇒ 00:16:10.965 Jakob Kagel: can everything chip out of every warehouse? Basically
152 00:16:11.510 ⇒ 00:16:17.325 Jakob Kagel: and like in theory, it should be able to. I feel like as long as like we send the inventory. But
153 00:16:17.590 ⇒ 00:16:18.280 Nicolas Sucari: Yeah.
154 00:16:18.280 ⇒ 00:16:21.933 Jakob Kagel: The short list, like of the actual warehouses.
155 00:16:22.630 ⇒ 00:16:24.092 Jakob Kagel: would be good
156 00:16:24.580 ⇒ 00:16:25.220 Nicolas Sucari: Okay, so.
157 00:16:25.220 ⇒ 00:16:25.870 Jakob Kagel: We can.
158 00:16:25.870 ⇒ 00:16:26.609 Nicolas Sucari: Let me! Let me!
159 00:16:26.610 ⇒ 00:16:35.059 Jakob Kagel: Cost savings like we can estimate the cost savings like with the when I did it like in notion to. It’s like I just did it based on the aggregate like
160 00:16:35.150 ⇒ 00:16:51.319 Jakob Kagel: cost like for that zone like for Florida, like Florida Zone 5. Compared to New York zone. 7 shipping cost, I mean, I think that’s a fair estimate. I mean, it’s obviously like it’s gonna depend more like on the product mix. But like on aggregate. That’s sort of like
161 00:16:51.710 ⇒ 00:16:53.969 Jakob Kagel: the best way to do it. In my opinion.
162 00:16:55.050 ⇒ 00:16:56.950 Nicolas Sucari: Yeah, yeah, yeah, I think that’s fine
163 00:16:56.960 ⇒ 00:16:58.080 Nicolas Sucari: dumb.
164 00:16:58.320 ⇒ 00:17:07.770 Nicolas Sucari: Okay, so let’s go through what we need like to try and close this out is, get that list of possible warehouses from Eunice right.
165 00:17:07.770 ⇒ 00:17:08.359 Jakob Kagel: Right.
166 00:17:08.759 ⇒ 00:17:13.039 Nicolas Sucari: Anything on the data side that we are still waiting on.
167 00:17:13.558 ⇒ 00:17:26.400 Jakob Kagel: I mean, there are okay. So I mean, there is I. I did notice this, too. I did bring this up. This is actually really important, we should talk about this. But like there are the the orders that are like for Florida
168 00:17:26.964 ⇒ 00:17:30.050 Jakob Kagel: are not in all order items.
169 00:17:30.110 ⇒ 00:17:32.300 Jakob Kagel: So it’s like a small
170 00:17:32.330 ⇒ 00:17:34.460 Jakob Kagel: percentage like of orders.
171 00:17:34.560 ⇒ 00:17:49.379 Jakob Kagel: But right now at least. But like there’s something structurally wrong there. And I I can show you like, I mean we can do it right now, like, so I can maybe show you like. It’s just like very simple. If we just take the shipments table.
172 00:17:51.660 ⇒ 00:17:52.820 Jakob Kagel: So
173 00:17:53.970 ⇒ 00:17:55.779 Jakob Kagel: you just say count.
174 00:17:57.730 ⇒ 00:18:00.299 Jakob Kagel: order id right.
175 00:18:02.240 ⇒ 00:18:06.570 Jakob Kagel: and then we’ll do warehouse. I think this warehouse state.
176 00:18:08.038 ⇒ 00:18:10.641 Jakob Kagel: Let’s check. But
177 00:18:13.350 ⇒ 00:18:16.260 Jakob Kagel: So from all order items.
178 00:18:17.810 ⇒ 00:18:19.389 Jakob Kagel: And then
179 00:18:22.620 ⇒ 00:18:27.220 Jakob Kagel: this is like something that I think is like the most concerning basically
180 00:18:38.830 ⇒ 00:18:40.320 Jakob Kagel: 100 and
181 00:18:52.283 ⇒ 00:18:58.919 Jakob Kagel: okay, let me just check real quick. Yes, warehouse state. Yeah, that’s right. Okay.
182 00:18:59.310 ⇒ 00:19:00.730 Jakob Kagel: So
183 00:19:01.130 ⇒ 00:19:05.499 Jakob Kagel: first, st I’m just not gonna do it with the join, like, I’m just gonna run it by itself.
184 00:19:06.241 ⇒ 00:19:08.889 Jakob Kagel: And I’m just gonna say, where
185 00:19:10.660 ⇒ 00:19:12.020 Jakob Kagel: ship date
186 00:19:12.720 ⇒ 00:19:14.290 Jakob Kagel: greater than
187 00:19:20.680 ⇒ 00:19:26.417 Jakob Kagel: okay. So this is like, in the last 2 years. Yeah. Oh, yeah. Kind of group. By yeah.
188 00:19:32.130 ⇒ 00:19:36.290 Jakob Kagel: right? So if I do this, I have like 600 orders like from Florida. Right.
189 00:19:36.340 ⇒ 00:19:37.389 Jakob Kagel: you see, may.
190 00:19:37.740 ⇒ 00:19:38.480 Nicolas Sucari: Yeah.
191 00:19:40.450 ⇒ 00:19:41.920 Jakob Kagel: and then
192 00:19:42.210 ⇒ 00:19:43.960 Jakob Kagel: if I join it.
193 00:19:47.570 ⇒ 00:19:54.000 Jakob Kagel: to the all order items just, and I’m just joining on order id right like I’m not doing anything but joining on order. Id.
194 00:19:54.000 ⇒ 00:19:54.630 Nicolas Sucari: Yeah.
195 00:19:55.090 ⇒ 00:19:56.370 Jakob Kagel: Oh.
196 00:19:57.390 ⇒ 00:19:58.700 Jakob Kagel: as O
197 00:19:58.840 ⇒ 00:19:59.890 Jakob Kagel: on
198 00:20:00.670 ⇒ 00:20:01.530 Jakob Kagel: yeah.
199 00:20:02.290 ⇒ 00:20:05.220 Jakob Kagel: And I just then it’s like I don’t have any.
200 00:20:07.550 ⇒ 00:20:08.547 Nicolas Sucari: In Florida. Yeah.
201 00:20:08.880 ⇒ 00:20:09.800 Jakob Kagel: Orders. Yeah.
202 00:20:11.280 ⇒ 00:20:15.860 Jakob Kagel: So that’s like telling me that those order ids just don’t exist
203 00:20:16.580 ⇒ 00:20:17.700 Jakob Kagel: in
204 00:20:17.880 ⇒ 00:20:18.730 Jakob Kagel: New York.
205 00:20:18.730 ⇒ 00:20:19.600 Nicolas Sucari: Controller.
206 00:20:19.600 ⇒ 00:20:25.279 Jakob Kagel: Or like, they’re getting like, yeah, I don’t know. They’re getting misassigned somehow.
207 00:20:27.030 ⇒ 00:20:32.229 Jakob Kagel: but that is yeah. I think that is like a big one that we really need to
208 00:20:32.940 ⇒ 00:20:55.840 Jakob Kagel: figure out. I mean right now, like, for the purposes of the analysis like that we’re doing. I mean, I’m just left joining right like on all order items. So I’m not excluding any of the Florida orders. But like I can’t get any. Basically like skew, level or product class like cuts on Florida orders. And again, it’s only like a couple of 100 orders right now. But that will obviously like be an issue like if we.
209 00:20:55.840 ⇒ 00:20:56.720 Nicolas Sucari: Yeah, yeah.
210 00:20:56.920 ⇒ 00:20:57.570 Jakob Kagel: Florida.
211 00:20:57.570 ⇒ 00:20:59.899 Nicolas Sucari: So what do what do we need is like to
212 00:21:00.060 ⇒ 00:21:01.266 Nicolas Sucari: review that
213 00:21:01.870 ⇒ 00:21:04.652 Jakob Kagel: Somebody needs to review that exactly like
214 00:21:05.770 ⇒ 00:21:10.430 Nicolas Sucari: It’s it’s the All order items, table. And what’s happening with the Florida warehouse.
215 00:21:11.090 ⇒ 00:21:18.550 Jakob Kagel: Well, the thing I mean, the way they just need to check is basically like the order Id is not in there like the order Id for is.
216 00:21:18.550 ⇒ 00:21:19.110 Nicolas Sucari: But in the.
217 00:21:19.110 ⇒ 00:21:23.469 Jakob Kagel: Our Florida orders that’s in shipments is not in all order items.
218 00:21:24.110 ⇒ 00:21:26.519 Jakob Kagel: Okay, that’s that’s what’s happening. So I mean.
219 00:21:26.520 ⇒ 00:21:30.069 Nicolas Sucari: We need to get the order Id from the shipments table.
220 00:21:30.160 ⇒ 00:21:31.979 Nicolas Sucari: The ones that are from Florida.
221 00:21:32.150 ⇒ 00:21:32.930 Jakob Kagel: Right. I mean.
222 00:21:32.930 ⇒ 00:21:34.319 Nicolas Sucari: That into all that I need to.
223 00:21:34.320 ⇒ 00:21:47.590 Jakob Kagel: The way that I’m the way that I’m understanding this like when I query it is that Florida orders are not in all order items period like they’re just no orders in there from Florida warehouse like we’re only getting New York orders
224 00:21:47.690 ⇒ 00:21:49.230 Jakob Kagel: in all our items.
225 00:21:53.720 ⇒ 00:21:54.400 Nicolas Sucari: Okay.
226 00:21:56.140 ⇒ 00:21:57.990 Nicolas Sucari: yeah, yeah. And this is pretty
227 00:21:58.090 ⇒ 00:21:59.960 Nicolas Sucari: important now, because
228 00:22:00.350 ⇒ 00:22:03.510 Nicolas Sucari: I don’t think it’s gonna make like, make changes. But
229 00:22:03.630 ⇒ 00:22:08.679 Nicolas Sucari: we have 600 and something. So yeah, probably we should take a look into that.
230 00:22:08.910 ⇒ 00:22:22.727 Jakob Kagel: Yeah, exactly. I mean, it’s not like we can. Still, because, like, we still have them in the shipments table. So we can still do things like for this analysis, like, you know, based on the total order value and things like that like, we’re not excluding anything like
231 00:22:23.200 ⇒ 00:22:25.980 Jakob Kagel: like. Because, like I said, I’m I’m left joining
232 00:22:26.462 ⇒ 00:22:30.777 Jakob Kagel: for those skew level cuts and stuff like we can’t do it. And
233 00:22:31.680 ⇒ 00:22:36.329 Jakob Kagel: It just is more. This is more of a thing that’s like. If we don’t deal with it now, it’ll bite us in the future.
234 00:22:36.330 ⇒ 00:22:37.130 Nicolas Sucari: Yeah, yeah.
235 00:22:37.130 ⇒ 00:22:43.939 Jakob Kagel: It doesn’t really the analysis cause we have them in the shipments table. And that’s mainly what we use. But yeah, we definitely like should try to.
236 00:22:44.060 ⇒ 00:22:44.940 Jakob Kagel: Okay.
237 00:22:45.820 ⇒ 00:22:52.590 Nicolas Sucari: What else? And do we need something from modeling? Or, yeah, we need to create something else.
238 00:22:52.600 ⇒ 00:22:54.210 Nicolas Sucari: And we have a warehouse.
239 00:22:54.210 ⇒ 00:23:03.790 Jakob Kagel: I was like the center of gravity, like using the script that Utam suggested. So I’m gonna do basically second iteration. This a little bit more like, you know.
240 00:23:03.870 ⇒ 00:23:08.520 Jakob Kagel: I haven’t looked that closely at the script yet, because I just haven’t had time, but
241 00:23:08.660 ⇒ 00:23:23.119 Jakob Kagel: I think that the idea was that it will account for the existing warehouse locations a little bit more. I honestly don’t think it’s gonna move the needle that much. I really kind of feel like the location that we got is probably like makes sense like.
242 00:23:23.120 ⇒ 00:23:23.440 Nicolas Sucari: Yeah.
243 00:23:23.440 ⇒ 00:23:42.860 Jakob Kagel: Based on the data that we have. It makes sense. But I don’t know. I think it’s worth trying it, and I don’t think it’ll be that hard to do. Just basically have to like, you know, reformat some data, or or make sure that you know we can feed it into the script. But I’ll do that as well. But I think for me it’s like those 2 things. It’s like the short list of the warehouses
244 00:23:42.910 ⇒ 00:23:43.790 Jakob Kagel: and
245 00:23:45.580 ⇒ 00:23:50.239 Nicolas Sucari: This. Okay to Florida on your orders. Okay, and about
246 00:23:50.460 ⇒ 00:23:57.520 Nicolas Sucari: about the warehouses. Do we have the will? Do. We have the the new warehouses in the table. Yet or not.
247 00:23:58.210 ⇒ 00:24:01.539 Jakob Kagel: Sorry. What are you asking exactly like? Do we have the location of the warehouses.
248 00:24:01.540 ⇒ 00:24:02.170 Nicolas Sucari: Yeah, I’m.
249 00:24:02.170 ⇒ 00:24:05.810 Jakob Kagel: Yeah, we do. It’s a
250 00:24:06.050 ⇒ 00:24:07.400 Jakob Kagel: I think. Yeah.
251 00:24:07.490 ⇒ 00:24:12.401 Jakob Kagel: Oh, no, this is one I made sorry. Hold on, but we do have it in one
252 00:24:14.150 ⇒ 00:24:17.279 Jakob Kagel: zip codes distance from warehouse mapping.
253 00:24:17.820 ⇒ 00:24:18.620 Jakob Kagel: because.
254 00:24:18.620 ⇒ 00:24:19.510 Nicolas Sucari: Remember.
255 00:24:19.510 ⇒ 00:24:27.180 Jakob Kagel: There is one. Yeah. Brian shared it with me, and I used it. I remember the table name off the top of my head, but we do have it. Yeah.
256 00:24:27.180 ⇒ 00:24:28.240 Nicolas Sucari: Excellent. Okay?
257 00:24:28.380 ⇒ 00:24:29.290 Nicolas Sucari: Anything else?
258 00:24:29.290 ⇒ 00:24:36.740 Jakob Kagel: And it’s also like warehouse names, or sorry, I mean, if you let me keep on, I’ll probably be here all day. But, like
259 00:24:36.930 ⇒ 00:24:40.644 Jakob Kagel: let’s just look at this one thing real quick to one more time.
260 00:24:40.930 ⇒ 00:24:41.550 Nicolas Sucari: Yeah, it’s.
261 00:24:41.550 ⇒ 00:24:43.709 Jakob Kagel: So if we just look at the warehouse
262 00:24:44.040 ⇒ 00:24:55.764 Jakob Kagel: like name, like distinct, I probably I mean I raised this already. I so it’s probably already a ticket, but like I don’t know. I just wanna sort of point this out again.
263 00:24:56.570 ⇒ 00:25:02.050 Jakob Kagel: that cause I’m using warehouse state right now. Not the warehouse name, because the warehouse.
264 00:25:02.050 ⇒ 00:25:02.770 Nicolas Sucari: Name it.
265 00:25:02.770 ⇒ 00:25:15.179 Jakob Kagel: Kind of stuff in it that is not the warehouse like this is the only warehouse basically like for New York, as I’m understanding it, and a lot of these, maybe, don’t have like any orders or whatever you know.
266 00:25:16.300 ⇒ 00:25:20.010 Nicolas Sucari: Jackson. Jacksonville is not another. The one in Florida.
267 00:25:20.700 ⇒ 00:25:26.140 Jakob Kagel: Yeah. Jacksonville is probably the Florida one. Yeah, but I don’t know what the other ones are, and it’s just like.
268 00:25:26.560 ⇒ 00:25:40.140 Jakob Kagel: I don’t know. I mean, I think it looks better than it did before, I think before we had over 100, so I don’t know if somebody worked on it and like cleaned up the list. But like the 1st time that I queried it, I think I had like a hundred warehouse names, and like
269 00:25:40.380 ⇒ 00:25:43.480 Jakob Kagel: they’re just all like not the warehouse, so.
270 00:25:43.480 ⇒ 00:25:44.040 Nicolas Sucari: Me!
271 00:25:44.040 ⇒ 00:25:47.000 Jakob Kagel: So how this is feeding in or whatnot. And
272 00:25:47.010 ⇒ 00:25:58.769 Jakob Kagel: I’m operating under the assumption that using Warehouse State, New York, is like safe to use. But this is just sort of another thing. That kind of is a data integrity thing where it’s like. I don’t know. This doesn’t look.
273 00:25:58.770 ⇒ 00:26:02.429 Nicolas Sucari: Okay, so this is this is warehouse naming shipment stable. Right?
274 00:26:02.430 ⇒ 00:26:08.489 Jakob Kagel: Right? Exactly. This is just the warehouse like column. It’s just called Warehouse. But yeah, it’s the warehouse name in the show.
275 00:26:08.490 ⇒ 00:26:10.469 Nicolas Sucari: Okay, needs to be cleaned out
276 00:26:10.710 ⇒ 00:26:12.760 Nicolas Sucari: or understand? Okay.
277 00:26:15.020 ⇒ 00:26:17.709 Nicolas Sucari: yeah, perfect. Okay. I’m gonna try to
278 00:26:19.074 ⇒ 00:26:24.619 Nicolas Sucari: get from Michael, from Eunice that list of possible warehouses and
279 00:26:24.660 ⇒ 00:26:31.650 Nicolas Sucari: the other 2 things I want. I will try to create a ticket and ask Brian probably help us with that. Okay.
280 00:26:31.930 ⇒ 00:26:38.509 Jakob Kagel: Yeah. Sounds good. Okay, cool? Well, I’ll probably just like, put in the shipments Channel. Just kind of like some of the key takeaways.
281 00:26:38.510 ⇒ 00:26:39.030 Nicolas Sucari: Yeah.
282 00:26:39.030 ⇒ 00:26:40.146 Jakob Kagel: Put in notion.
283 00:26:40.890 ⇒ 00:26:44.030 Nicolas Sucari: I think sending a message to Chuck will be.
284 00:26:44.030 ⇒ 00:26:45.699 Jakob Kagel: Larger conversation therein.
285 00:26:45.700 ⇒ 00:26:46.290 Nicolas Sucari: Yeah.
286 00:26:46.400 ⇒ 00:26:51.219 Nicolas Sucari: perfect. Thank you, Jacob. I think you’re we’re we’re good with this.
287 00:26:51.690 ⇒ 00:26:57.229 Jakob Kagel: Yeah, yeah, I think we’re we’re on. We’re on good progress. So yeah, sounds good. Alright. We’ll talk soon. See? Ya.
288 00:26:57.230 ⇒ 00:26:58.410 Nicolas Sucari: Thank you. Bye, bye.