Meeting Title: Analysis-Planning-Session Date: 2024-07-08 Meeting participants: Nicolas Sucari, Uttam Kumaran, Jakob Kagel
WEBVTT
1 00:00:18.540 ⇒ 00:00:19.610 Uttam Kumaran: Hey? Akshay.
2 00:00:22.380 ⇒ 00:00:23.110 Akshay kumar.G: Body.
3 00:00:23.470 ⇒ 00:00:24.859 Uttam Kumaran: Hey? Good! How are you?
4 00:00:25.560 ⇒ 00:00:26.110 Akshay kumar.G: Yeah.
5 00:00:46.114 ⇒ 00:00:46.500 Nicolas Sucari: Guys.
6 00:00:46.950 ⇒ 00:00:47.760 Uttam Kumaran: And it.
7 00:00:52.930 ⇒ 00:00:57.339 Nicolas Sucari: Leave my camera off because I’m still cooking something. Switch.
8 00:02:15.440 ⇒ 00:02:18.570 Uttam Kumaran: I think Jacob might be a little delayed.
9 00:02:22.110 ⇒ 00:02:22.880 Nicolas Sucari: I’m ready.
10 00:02:26.180 ⇒ 00:02:28.509 Uttam Kumaran: So maybe you would just wait until
11 00:02:28.870 ⇒ 00:02:32.500 Uttam Kumaran: he said. He’s in a meeting at one just right now.
12 00:02:32.640 ⇒ 00:02:34.759 Uttam Kumaran: so maybe we just wait until he’s free.
13 00:02:49.170 ⇒ 00:02:50.870 Nicolas Sucari: You don’t work here.
14 00:02:52.300 ⇒ 00:03:00.339 Nicolas Sucari: I why, github desktop is like asking me the pass phrase, every you know
15 00:03:00.890 ⇒ 00:03:06.590 Nicolas Sucari: 10 min, or something like that I think every time you merge something as I need like to pull.
16 00:03:06.720 ⇒ 00:03:09.679 Nicolas Sucari: like to fetch the origin again right? Like to
17 00:03:09.750 ⇒ 00:03:11.360 Nicolas Sucari: sync up the repo
18 00:03:11.420 ⇒ 00:03:12.850 Nicolas Sucari: like I need to
19 00:03:14.320 ⇒ 00:03:16.809 Nicolas Sucari: put my passphrase every time.
20 00:03:16.820 ⇒ 00:03:18.419 Nicolas Sucari: Is that how it works?
21 00:03:18.730 ⇒ 00:03:20.789 Uttam Kumaran: No, it’s not. It doesn’t ask me to do that.
22 00:03:21.650 ⇒ 00:03:24.782 Nicolas Sucari: Okay. I don’t know why. Then it’s asking me
23 00:03:25.270 ⇒ 00:03:27.660 Nicolas Sucari: my passpay or my key
24 00:03:27.860 ⇒ 00:03:29.090 Nicolas Sucari: little time.
25 00:03:29.090 ⇒ 00:03:29.820 Uttam Kumaran: Hmm.
26 00:03:29.820 ⇒ 00:03:30.979 Nicolas Sucari: Really weird.
27 00:03:31.670 ⇒ 00:03:37.320 Uttam Kumaran: Yeah, maybe it’s something like it doesn’t ask me anything. I don’t know whether should. Maybe
28 00:03:38.810 ⇒ 00:03:43.499 Uttam Kumaran: if you go to settings, there’s a way to like log in, and things like that. I’m not exactly sure.
29 00:03:46.040 ⇒ 00:03:46.730 Nicolas Sucari: Or ray.
30 00:03:53.450 ⇒ 00:03:59.332 Uttam Kumaran: Okay, maybe we just I’m just. I’m just continuing to work on some stuff. So maybe we just stay on until
31 00:04:00.550 ⇒ 00:04:02.770 Uttam Kumaran: until Jacob gets here.
32 00:04:03.560 ⇒ 00:04:04.180 Nicolas Sucari: Nay.
33 00:04:04.520 ⇒ 00:04:05.779 Nicolas Sucari: okay. Yeah. Sure.
34 00:04:06.300 ⇒ 00:04:06.990 Nicolas Sucari: Okay.
35 00:29:26.530 ⇒ 00:29:27.749 Jakob Kagel: Hey? How’s it going? Y’all.
36 00:29:28.340 ⇒ 00:29:29.205 Uttam Kumaran: Yo.
37 00:29:30.395 ⇒ 00:29:30.720 Nicolas Sucari: Gog.
38 00:29:32.450 ⇒ 00:29:33.282 Jakob Kagel: How are you doing.
39 00:29:35.170 ⇒ 00:29:35.680 Uttam Kumaran: Dude.
40 00:29:37.370 ⇒ 00:29:39.840 Jakob Kagel: Don’t sound too fired up for Monday.
41 00:29:40.612 ⇒ 00:29:42.420 Uttam Kumaran: Sorry. I just like
42 00:29:43.202 ⇒ 00:29:44.147 Uttam Kumaran: just working.
43 00:29:45.186 ⇒ 00:29:53.689 Jakob Kagel: I, I understand. Okay, cool. Yeah. Where do we want to start? Kinda.
44 00:29:56.260 ⇒ 00:29:58.360 Uttam Kumaran: I’m down for
45 00:30:00.270 ⇒ 00:30:01.510 Uttam Kumaran: anywhere, I guess.
46 00:30:01.510 ⇒ 00:30:05.681 Jakob Kagel: Okay? Well, then, yeah, let’s talk about then, real quick. Right?
47 00:30:06.770 ⇒ 00:30:26.729 Jakob Kagel: so this is the thing. Okay? So 1st of all, it’s great. We got that long in. I just like pulled like a script to do the center of gravity. So I should hopefully be able to do that pretty quickly. If not today. By like end of day tomorrow. The thing I’m seeing like from like looking at the data is like.
48 00:30:26.870 ⇒ 00:30:39.750 Jakob Kagel: okay, obviously, like the pumps are like the most high value items, but the thing that we ship like by far the most is brushes, and that’s where we’re losing all the money in shipments is in brushes.
49 00:30:41.790 ⇒ 00:30:42.989 Jakob Kagel: so I don’t know.
50 00:30:42.990 ⇒ 00:30:45.380 Uttam Kumaran: Losing all, losing all the money, meaning.
51 00:30:45.380 ⇒ 00:30:57.813 Jakob Kagel: Well, it’s like the most expensive to ship. Basically, like we ship, like they all ship basically usps. I know. I know we talked about this a little bit before, and this is like where I wanted to kinda like circle back to it.
52 00:30:58.210 ⇒ 00:30:59.449 Jakob Kagel: is like.
53 00:31:01.130 ⇒ 00:31:19.729 Jakob Kagel: yeah, I mean, cause I’m looking like all these splits on like, okay, which cities have like the furthest like in in Texas, like it’s like, who has, like the longest distance like who has the longest zone. And then it’s like, what like products are we shipping like to these cities? And
54 00:31:19.970 ⇒ 00:31:28.740 Jakob Kagel: I don’t know. It just always comes back kind of that, like the brushes are like the the main thing. Kind of that we ship, and that’s kind of, in my opinion, is like.
55 00:31:28.960 ⇒ 00:31:36.604 Jakob Kagel: I’m wondering like, is there an opportunity there like? Is there a reason why we have to ship the brushes like usps? Basically, or like.
56 00:31:36.910 ⇒ 00:31:39.860 Uttam Kumaran: Because of the it’s they’re only like a pound.
57 00:31:39.860 ⇒ 00:31:40.650 Jakob Kagel: Right.
58 00:31:41.160 ⇒ 00:31:43.160 Uttam Kumaran: So it’s mainly the weight.
59 00:31:43.170 ⇒ 00:31:46.809 Uttam Kumaran: But I’m pretty sure we’re like. So
60 00:31:47.120 ⇒ 00:31:52.249 Uttam Kumaran: I I thought, like we’re spending the most, though, on
61 00:31:52.880 ⇒ 00:31:57.350 Uttam Kumaran: like historically, we’re spending the most in terms of shipping cost.
62 00:31:57.620 ⇒ 00:31:57.970 Jakob Kagel: Right.
63 00:31:58.300 ⇒ 00:31:59.490 Uttam Kumaran: The pumps.
64 00:31:59.920 ⇒ 00:32:27.199 Jakob Kagel: Yeah, I mean, we are like, you know, but it’s like I I and I mean I I have to check this. I don’t know the numbers like at the top of my head. And that’s why I just wanted to talk about it. It’s like we ship like, yeah, we ship like a couple of 100 punch. We ship like thousands of brushes like in California, Texas, and Florida. So I don’t know. I have to check. I mean, I’m gonna check sort of on the aggregate like shipping numbers, I think, for these 2 classes.
65 00:32:27.511 ⇒ 00:32:31.168 Jakob Kagel: Let me. Actually, I’ll just pull it up right now let’s take a look.
66 00:32:34.110 ⇒ 00:32:38.354 Jakob Kagel: and we can see. Oh, sorry to hook up my monitor.
67 00:32:38.740 ⇒ 00:32:42.740 Uttam Kumaran: Cause you’re I mean you’re right in that. We do ship a lot of volume, but.
68 00:32:42.740 ⇒ 00:32:43.370 Jakob Kagel: Right.
69 00:32:43.370 ⇒ 00:32:44.760 Uttam Kumaran: It’s like, but it.
70 00:32:44.760 ⇒ 00:32:47.618 Jakob Kagel: The volume’s like insignificant, basically,
71 00:32:48.190 ⇒ 00:32:50.640 Uttam Kumaran: More like if if it’s
72 00:32:50.780 ⇒ 00:32:53.410 Uttam Kumaran: like, for example, if it’s cost like.
73 00:32:53.460 ⇒ 00:32:59.479 Uttam Kumaran: if the brushes cost 30 bucks to ship, and then there’s it costs us like some amount.
74 00:33:00.050 ⇒ 00:33:04.619 Uttam Kumaran: It it’s the brushes cost some amount to ship, and of course there’s a large volume.
75 00:33:04.640 ⇒ 00:33:07.800 Uttam Kumaran: That’s so. May I guess I want to know, like, what is
76 00:33:08.210 ⇒ 00:33:13.080 Uttam Kumaran: yeah, like, what is the opportunity? Because what we did is we moved that some of it to ups.
77 00:33:13.650 ⇒ 00:33:18.339 Uttam Kumaran: But again, like, yeah, it’s high volume, but total amounts are low.
78 00:33:18.530 ⇒ 00:33:19.449 Jakob Kagel: And right available.
79 00:33:21.480 ⇒ 00:33:25.620 Jakob Kagel: I mean, yeah, it’s true. I mean, it’s a lot of like.
80 00:33:26.290 ⇒ 00:33:39.988 Jakob Kagel: That’s why it’s like a little tricky is because it’s like we’ll have like one pump that goes to like one city, you know, and it’s like very expensive to ship to that city. But it’s like all the volume is kind of like in the brushes.
81 00:33:41.750 ⇒ 00:33:50.260 Jakob Kagel: I mean, I think I’m close to like, you know, kinda having like the idea figured out. But it’s just helpful kind of like to talk through it real quick.
82 00:33:50.260 ⇒ 00:33:52.568 Uttam Kumaran: Yeah, I I guess, like
83 00:33:53.050 ⇒ 00:34:00.199 Uttam Kumaran: The reason what we did is we there was, we’re we’re just spending so much on shipping the pumps.
84 00:34:00.736 ⇒ 00:34:11.159 Uttam Kumaran: That we we renegotiated move from Fedex to Ups, and that saved us a bunch. I guess what we’re trying to find here is maybe
85 00:34:11.400 ⇒ 00:34:12.550 Uttam Kumaran: less.
86 00:34:12.870 ⇒ 00:34:15.330 Uttam Kumaran: I mean, I don’t know. I think it’s less about
87 00:34:15.760 ⇒ 00:34:19.330 Uttam Kumaran: an individual item as a whole, but more of like
88 00:34:20.702 ⇒ 00:34:23.420 Uttam Kumaran: like, what is the zone impact.
89 00:34:23.420 ⇒ 00:34:24.130 Jakob Kagel: Right.
90 00:34:24.130 ⇒ 00:34:34.150 Uttam Kumaran: It’s like less about okay. If we were to lower the shipping costs on one item, you keep that standard, then it’s basically our lever here, and this decision is like.
91 00:34:34.219 ⇒ 00:34:50.750 Uttam Kumaran: can we get the zone to Zone 3 for the most amount of shipping. Spend right? Like, if you segment, if we can get 40 of our shipping spend moved from like zone 5 to Zone 3. Then, you know, it’s going to go lower. Right? So, for example, if you were to say like.
92 00:34:50.889 ⇒ 00:34:53.550 Uttam Kumaran: by putting a thing in in Texas.
93 00:34:53.780 ⇒ 00:34:54.940 Uttam Kumaran: we end up
94 00:34:55.040 ⇒ 00:34:57.150 Uttam Kumaran: like historically, we would have
95 00:34:57.628 ⇒ 00:34:59.420 Uttam Kumaran: shipped. We would have like
96 00:34:59.460 ⇒ 00:35:01.150 Uttam Kumaran: been moved like
97 00:35:01.210 ⇒ 00:35:04.260 Uttam Kumaran: half of our zone. 5 packages into Zone 2.
98 00:35:04.550 ⇒ 00:35:06.609 Uttam Kumaran: That’s, I think. Yeah.
99 00:35:06.850 ⇒ 00:35:10.168 Jakob Kagel: Okay. Well, here, let me share my screen real quick.
100 00:35:12.040 ⇒ 00:35:13.770 Jakob Kagel: cool. You can see my screen.
101 00:35:15.740 ⇒ 00:35:16.560 Uttam Kumaran: No!
102 00:35:16.760 ⇒ 00:35:18.660 Jakob Kagel: Oh, whoops is not working! Hold on!
103 00:35:19.170 ⇒ 00:35:20.350 Jakob Kagel: Is it working now?
104 00:35:21.250 ⇒ 00:35:21.840 Nicolas Sucari: Yep.
105 00:35:22.170 ⇒ 00:35:23.040 Jakob Kagel: Okay?
106 00:35:23.300 ⇒ 00:35:28.769 Jakob Kagel: So right? So like, this is for Texas, right? So we have, like, average shipping costs like
107 00:35:29.030 ⇒ 00:35:41.609 Jakob Kagel: 32 for like in ground pool pumps like for the brushes. It’s definitely not that much. It’s like 10, but it’s like 7,000 orders.
108 00:35:43.040 ⇒ 00:35:45.650 Jakob Kagel: it’s like the vast vast majority. Yeah.
109 00:35:46.970 ⇒ 00:35:50.600 Jakob Kagel: And I mean, I get it. It’s like there. This, the reason, like the price per pound.
110 00:35:50.600 ⇒ 00:35:52.309 Uttam Kumaran: Oh, this is just in Texas.
111 00:35:52.480 ⇒ 00:35:53.920 Jakob Kagel: Right? This is just Texas.
112 00:35:54.100 ⇒ 00:35:57.366 Jakob Kagel: Yeah, this is, this is limited to Texas. Yeah.
113 00:35:57.730 ⇒ 00:36:00.910 Uttam Kumaran: Okay? So it’s like 70 K of Texas.
114 00:36:02.070 ⇒ 00:36:03.920 Uttam Kumaran: right? And.
115 00:36:10.440 ⇒ 00:36:19.430 Jakob Kagel: because it’s like I mean, the thing is like that’s tricky, too, is like, I don’t know. I mean, you have more experience. Kind of like negotiating the shipping to or like whatnot, but it’s like
116 00:36:19.860 ⇒ 00:36:31.920 Jakob Kagel: it’s like, I don’t know how much like leverage we can have sort of like with shipping negotiation if we’re not shipping like a high volume of pumps, kind of, or or things like to like one specific area.
117 00:36:32.010 ⇒ 00:36:33.640 Jakob Kagel: But I guess, like I.
118 00:36:33.640 ⇒ 00:36:37.749 Uttam Kumaran: I guess what I don’t understand is like, why, look at the number of
119 00:36:38.230 ⇒ 00:36:42.730 Uttam Kumaran: like. Why, look at the number of orders going to Texas here.
120 00:36:42.820 ⇒ 00:36:44.980 Uttam Kumaran: Instead, it’s it’s like
121 00:36:49.840 ⇒ 00:36:58.529 Uttam Kumaran: Instead, it’s like, should we? We should be looking at if we were to move this to Zone 3. What is the impact on the average shipping cost right? Like.
122 00:36:58.930 ⇒ 00:37:09.250 Jakob Kagel: Okay, but how do we do that? Then? Cause like we can just say, like, like, do you like you wanna just filter on zone 3, basically or like.
123 00:37:10.070 ⇒ 00:37:10.989 Jakob Kagel: because it’s like.
124 00:37:10.990 ⇒ 00:37:13.650 Uttam Kumaran: I guess, like what I guess like. So let’s say we have.
125 00:37:13.860 ⇒ 00:37:22.130 Uttam Kumaran: So this stuff from Texas, the average shipping costs. You’re seeing part of the contribution of that is because it’s getting shipped from New York.
126 00:37:22.320 ⇒ 00:37:23.020 Jakob Kagel: Right.
127 00:37:23.020 ⇒ 00:37:26.730 Uttam Kumaran: So so yeah, there’s an average currently. But that average.
128 00:37:26.810 ⇒ 00:37:34.240 Uttam Kumaran: What we’re trying to see is like, what is the impact to that average? If and you have it. There you have. That average is like 7.
129 00:37:34.240 ⇒ 00:37:35.319 Jakob Kagel: Right. They’re all basically.
130 00:37:35.320 ⇒ 00:37:40.829 Uttam Kumaran: Brutal. So what so we’re trying to do is basically say, if we were to bring that to 3,
131 00:37:41.090 ⇒ 00:37:47.199 Uttam Kumaran: how does that impact the average shipping cost. And then basically, how does that impact the price per pound? And like.
132 00:37:47.220 ⇒ 00:37:49.369 Uttam Kumaran: I’ve got total estimated savings.
133 00:37:49.370 ⇒ 00:38:03.719 Jakob Kagel: Okay, well, let’s talk about it real quick. Then, like so I mean, how can we factor like like, how do we? Obviously, we can do like the price per pound where it’s like we have like the shipment cost, and then we have, like the shipment. Wait.
134 00:38:03.730 ⇒ 00:38:04.820 Jakob Kagel: I mean.
135 00:38:05.120 ⇒ 00:38:22.319 Jakob Kagel: can we say like, is it fair to say, if we do like the the shipping cost divided by the zones, basically that that’s like the price per zone. And then we can say that, okay, if we go from 7 to 3, then our cost should reduce that much, or is that not like kind of like fair.
136 00:38:24.680 ⇒ 00:38:25.949 Jakob Kagel: You see what I’m saying?
137 00:38:25.950 ⇒ 00:38:26.620 Uttam Kumaran: Yeah.
138 00:38:33.430 ⇒ 00:38:39.950 Uttam Kumaran: Yeah. So I think, like, for example, there’s a cost. There’s a difference in the cost for us to ship the pump
139 00:38:40.060 ⇒ 00:38:46.810 Uttam Kumaran: to mass versus to California. So the one thing is like isolating that variable which is basically saying
140 00:38:46.860 ⇒ 00:38:48.220 Uttam Kumaran: for each
141 00:38:49.010 ⇒ 00:38:52.649 Uttam Kumaran: the the, the. This is the problem is that, like the zone
142 00:38:55.730 ⇒ 00:38:59.119 Uttam Kumaran: does. The impact of zone on shipping a brush.
143 00:38:59.390 ⇒ 00:39:02.480 Uttam Kumaran: To those 2 areas versus shipping a pump is different.
144 00:39:02.640 ⇒ 00:39:02.980 Jakob Kagel: Right.
145 00:39:02.980 ⇒ 00:39:10.540 Uttam Kumaran: Some, there is some segmentation by product class that needs to happen. However, you can get basically.
146 00:39:10.750 ⇒ 00:39:16.380 Uttam Kumaran: yeah, the average cost per zone per zone, or it’s almost like
147 00:39:16.760 ⇒ 00:39:24.709 Uttam Kumaran: it’s almost like, for example, for zone 1, 2, 3, 4, 5, 6. What is the average price per pound? Basically.
148 00:39:25.190 ⇒ 00:39:27.770 Jakob Kagel: Okay, so group group by zone basically.
149 00:39:27.770 ⇒ 00:39:35.250 Uttam Kumaran: Group by zone, so that you can then say, basically, if we were to move this amount of orders
150 00:39:36.060 ⇒ 00:39:40.609 Uttam Kumaran: to from this zone to this zone you can then expect
151 00:39:41.320 ⇒ 00:39:49.040 Uttam Kumaran: like that. Multiplication did the difference in savings, for example, what I’m seeing is like, yeah, most of our stuff, like
152 00:39:49.090 ⇒ 00:39:56.759 Uttam Kumaran: most of what they’ve done the last 2 years is zone 5, 6, and 8 in terms of the total number of shipments.
153 00:39:57.290 ⇒ 00:39:58.040 Jakob Kagel: Yeah.
154 00:39:58.040 ⇒ 00:40:06.570 Uttam Kumaran: We want to try to get most of that to Zone 2, 3, and 4, because the average price per pound in zone 2 and 3 and 4.
155 00:40:06.690 ⇒ 00:40:11.099 Uttam Kumaran: It’s gonna be lower, right. But it’s I don’t think it’s helpful to look at.
156 00:40:11.280 ⇒ 00:40:15.529 Uttam Kumaran: It’s it’s you should look at average price per pound by product, class by zone.
157 00:40:16.080 ⇒ 00:40:16.890 Jakob Kagel: Okay.
158 00:40:17.580 ⇒ 00:40:19.819 Jakob Kagel: okay, that makes sense. Yeah.
159 00:40:19.820 ⇒ 00:40:26.189 Uttam Kumaran: Because then the goal is like cause. The the the real modeling that needs to be done is basically look at
160 00:40:26.220 ⇒ 00:40:29.490 Uttam Kumaran: all of the historical orders, and what it shipped from.
161 00:40:29.650 ⇒ 00:40:31.470 Uttam Kumaran: and then saying, and then.
162 00:40:31.690 ⇒ 00:40:42.410 Uttam Kumaran: ideally, what you have is like everything shipped from New York or Florida for all of the history. Then what you can say is, if you were to say, create a logic that’s basically shipped from
163 00:40:42.850 ⇒ 00:40:54.480 Uttam Kumaran: the closest one, which is one of the 4, 1 of the 4, then we can basically say, cool, what is our average zone? What is our average price per pound? And then what is our
164 00:40:54.660 ⇒ 00:40:58.909 Uttam Kumaran: total amount you’ll have, like, basically the actuals versus
165 00:40:59.690 ⇒ 00:41:02.750 Uttam Kumaran: like our actuals. If we were to have 4.
166 00:41:02.750 ⇒ 00:41:06.460 Jakob Kagel: Okay, yeah, makes sense. Okay, that’s good. Then.
167 00:41:06.460 ⇒ 00:41:09.428 Uttam Kumaran: So then. So then that’s why I think it’s I think it’s less about
168 00:41:10.030 ⇒ 00:41:22.339 Uttam Kumaran: It’s less about. If we it’s less about like, oh, can we reduce the price per pack? It’s less about the volume. It’s more just about the impact on zone of zone. Yeah.
169 00:41:22.530 ⇒ 00:41:24.146 Jakob Kagel: Okay, that makes sense.
170 00:41:24.550 ⇒ 00:41:31.340 Uttam Kumaran: Yeah, once, once we run out of optimizations on the warehouse is then
171 00:41:31.580 ⇒ 00:41:47.269 Uttam Kumaran: is then when we can go and say, Try to work with the chipping provider to just get it across the board lower. But this is going to be more about. Can we get people to a lower zone in order to reap the benefits of a lower price per pound for each
172 00:41:48.260 ⇒ 00:41:49.520 Uttam Kumaran: product. Class.
173 00:41:49.520 ⇒ 00:41:49.940 Jakob Kagel: Right.
174 00:41:49.940 ⇒ 00:42:00.049 Nicolas Sucari: It’s kind of. If logistically, we are doing something better, it will reduce the cost without reducing, like the rate that the ship shipping companies
175 00:42:00.100 ⇒ 00:42:01.420 Nicolas Sucari: charging us right.
176 00:42:03.240 ⇒ 00:42:06.769 Uttam Kumaran: Yeah, like, we’re not gonna be able to affect the rate we’re gonna consider the rate.
177 00:42:06.770 ⇒ 00:42:07.500 Nicolas Sucari: Exactly. Thank you.
178 00:42:07.500 ⇒ 00:42:08.410 Uttam Kumaran: For a jump.
179 00:42:08.920 ⇒ 00:42:09.610 Uttam Kumaran: but we got.
180 00:42:09.610 ⇒ 00:42:19.300 Nicolas Sucari: That’s why I mean, we’re just gonna going to understand from where we need to shave so that we can reduce that cost without touching the rate.
181 00:42:19.570 ⇒ 00:42:20.130 Uttam Kumaran: Yeah.
182 00:42:20.380 ⇒ 00:42:25.239 Jakob Kagel: Okay, okay, yeah. That’s good. Okay, sorry about the the confusion. There.
183 00:42:25.240 ⇒ 00:42:29.350 Uttam Kumaran: No, no, no, it makes sense, I mean again, you’re just like kind of seeing the whole picture on.
184 00:42:29.350 ⇒ 00:42:31.170 Jakob Kagel: Yeah, exactly.
185 00:42:31.170 ⇒ 00:42:33.870 Uttam Kumaran: I think the biggest thing that would be
186 00:42:34.000 ⇒ 00:42:40.510 Uttam Kumaran: that again we should see how close we get again. It’ll I think I think you’ll probably have. You’ll probably get like a
187 00:42:40.650 ⇒ 00:42:44.100 Uttam Kumaran: I don’t I? The thing I don’t know in my head how to do
188 00:42:44.130 ⇒ 00:42:46.109 Uttam Kumaran: is basically
189 00:42:48.400 ⇒ 00:42:51.910 Uttam Kumaran: like, how do you get the lat of the optimal
190 00:42:52.170 ⇒ 00:42:53.810 Uttam Kumaran: place, and then.
191 00:42:53.810 ⇒ 00:42:55.430 Jakob Kagel: Basically we would.
192 00:42:55.430 ⇒ 00:42:56.270 Uttam Kumaran: That went long.
193 00:42:56.270 ⇒ 00:43:02.368 Jakob Kagel: Center. That’s what the center of gravity, like analysis, is supposed to have as the output, basically like the.
194 00:43:02.690 ⇒ 00:43:09.650 Uttam Kumaran: So saying, if we get the lat long of where that one should be, we send that to Unis, and say, find the closest one to this.
195 00:43:09.650 ⇒ 00:43:23.623 Jakob Kagel: Right? Yeah, I just think, yeah, exactly. i i i think it’s good. I I definitely think we should do the center of gravity. And I mean, I’m I’m planning to do that. But I think, yeah, it’s just good to if we don’t use it as kind of like. The only thing like, you know. So that’s.
196 00:43:23.860 ⇒ 00:43:30.939 Uttam Kumaran: The other. The other factors that are there is like. If things are low volume, as Chuck said, he’s not. Gonna
197 00:43:31.320 ⇒ 00:43:34.469 Uttam Kumaran: it’s kind of waits until things become more like.
198 00:43:35.930 ⇒ 00:43:45.939 Uttam Kumaran: more cross country, like stuff that they’re just testing this low volume. They won’t do that. But again, it’s like we. I don’t want to over complicate it. The biggest thing here is like
199 00:43:46.520 ⇒ 00:43:48.530 Uttam Kumaran: in reduce in
200 00:43:48.580 ⇒ 00:43:54.269 Uttam Kumaran: in reducing it everything, from an average of 5 or 6 to 3 and 4.
201 00:43:54.300 ⇒ 00:43:58.269 Uttam Kumaran: What is our estimated cost savings? And
202 00:43:58.720 ⇒ 00:44:02.460 Uttam Kumaran: in order to do that, where does that 4th one need to be basically.
203 00:44:02.700 ⇒ 00:44:03.893 Jakob Kagel: Right, right.
204 00:44:04.490 ⇒ 00:44:15.410 Uttam Kumaran: You’re looking at a you are looking at an overlay of this on our past right? Because I don’t think you should use a future one. You just look at the last 2 years and say if we were to.
205 00:44:15.500 ⇒ 00:44:22.880 Uttam Kumaran: if we were to have these 4 locations and everything were to ship were to ship from their ideal place.
206 00:44:23.140 ⇒ 00:44:29.289 Uttam Kumaran: and what would be our average zone? And then what would that 4th place like need to be basically.
207 00:44:29.680 ⇒ 00:44:30.617 Jakob Kagel: Right, I guess.
208 00:44:31.145 ⇒ 00:44:32.650 Uttam Kumaran: I like how to get there, but.
209 00:44:32.650 ⇒ 00:44:47.989 Jakob Kagel: Right? Right? Yeah, that’s yet. So let’s talk about the zone like a little bit more. So if everything is like shipping from New York. Right? It’s all shipping from this one warehouse is like what I saw right in the same warehouse. So then, the zones are going to be relative
210 00:44:48.670 ⇒ 00:44:51.149 Jakob Kagel: to New York. So
211 00:44:52.500 ⇒ 00:44:53.649 Jakob Kagel: it’s like.
212 00:44:53.650 ⇒ 00:44:54.840 Uttam Kumaran: Oh, obviously.
213 00:44:54.840 ⇒ 00:45:01.619 Jakob Kagel: Gonna be a bias, probably towards like cities that are like
214 00:45:01.670 ⇒ 00:45:06.879 Jakob Kagel: further, like from New York, like that are on. I guess the west side of Texas.
215 00:45:06.880 ⇒ 00:45:14.800 Uttam Kumaran: Basically, you need some sort of like, basically, you need some sort of ranking that takes the closest warehouse to the customer.
216 00:45:14.940 ⇒ 00:45:15.610 Jakob Kagel: Right.
217 00:45:15.610 ⇒ 00:45:19.379 Uttam Kumaran: Based on the distance which is going to be some sort. Snowflake has
218 00:45:19.670 ⇒ 00:45:28.109 Uttam Kumaran: some distance, like, I think there’s like a geodist or something. There’s a function that basically you plug in the coordinates will give you that. But ideally.
219 00:45:28.110 ⇒ 00:45:28.630 Jakob Kagel: It, it.
220 00:45:28.630 ⇒ 00:45:29.360 Uttam Kumaran: Aches
221 00:45:29.490 ⇒ 00:45:31.800 Uttam Kumaran: it, it’ll say, Oh.
222 00:45:32.200 ⇒ 00:45:40.219 Uttam Kumaran: this client is, it will say it’ll calculate the distances between that client and all 4 of them, and then pick the one that’s closest. Basically.
223 00:45:40.330 ⇒ 00:45:45.810 Uttam Kumaran: once it picks the ones that that’s closest, you can then say, cool, what’s that zone
224 00:45:45.950 ⇒ 00:45:49.089 Uttam Kumaran: so that that zone will be distance from there.
225 00:45:50.390 ⇒ 00:45:57.239 Jakob Kagel: They would be like, you’re saying, like, pick the one closest out of like sort of a a list that we’ve already like pre identified right.
226 00:45:57.240 ⇒ 00:46:01.389 Uttam Kumaran: So we. So we do have the the pre identified list are the 4 warehouses.
227 00:46:01.980 ⇒ 00:46:04.750 Jakob Kagel: The ones that okay, that are all over the country right?
228 00:46:04.750 ⇒ 00:46:05.720 Uttam Kumaran: Typically you’re taking.
229 00:46:05.720 ⇒ 00:46:09.389 Jakob Kagel: But everything from Texas is only coming from New York right?
230 00:46:09.390 ⇒ 00:46:13.840 Uttam Kumaran: But that’s but that is because we don’t have the 4th location right.
231 00:46:13.840 ⇒ 00:46:14.520 Jakob Kagel: Right.
232 00:46:14.850 ⇒ 00:46:18.419 Uttam Kumaran: So the the the thing to then establish is.
233 00:46:18.650 ⇒ 00:46:21.789 Uttam Kumaran: where does that 4th one need to go
234 00:46:22.690 ⇒ 00:46:28.039 Uttam Kumaran: in order to then further further it right, like the 1st thing, I think is basically
235 00:46:28.380 ⇒ 00:46:39.059 Uttam Kumaran: taking all historical orders, all historical order addresses, and then saying, if we were to have had these ones online, which one would it have shipped from. So that’s probably like one
236 00:46:39.140 ⇒ 00:46:42.289 Uttam Kumaran: Ct. Or something. The next thing is then saying.
237 00:46:42.290 ⇒ 00:46:45.970 Jakob Kagel: Can you say that again? So you’re saying, if we had had them online.
238 00:46:46.190 ⇒ 00:46:56.130 Uttam Kumaran: Meaning in in the past like 2, let’s say, just past 2 years of data. We only had the New York one online until, like the last 6 months, where we had the Florida one.
239 00:46:56.400 ⇒ 00:46:56.880 Jakob Kagel: Okay.
240 00:46:56.880 ⇒ 00:47:01.990 Uttam Kumaran: What we’re what we’re trying to show is that for our historical orders, like, what? What?
241 00:47:02.150 ⇒ 00:47:06.620 Uttam Kumaran: Where? Based on our historical like shipment data
242 00:47:06.740 ⇒ 00:47:12.910 Uttam Kumaran: one we want to establish our savings in by having California, by having Florida
243 00:47:15.460 ⇒ 00:47:24.309 Uttam Kumaran: and by having New York. So, for example, instead of shipping all from New York, some amount would have been shipped by the California. Some amount would have been shipped by the Florida. Have we been using them.
244 00:47:24.650 ⇒ 00:47:25.430 Jakob Kagel: Okay.
245 00:47:25.430 ⇒ 00:47:26.010 Uttam Kumaran: Dean.
246 00:47:26.310 ⇒ 00:47:32.090 Uttam Kumaran: because because for a customer in Georgia it would have ranked and said, Oh, ship that from Florida.
247 00:47:32.410 ⇒ 00:47:33.070 Jakob Kagel: Okay.
248 00:47:33.070 ⇒ 00:47:36.029 Uttam Kumaran: So then, in that sense you can then assign that
249 00:47:36.700 ⇒ 00:47:38.200 Uttam Kumaran: shipment a zone.
250 00:47:38.920 ⇒ 00:47:43.360 Uttam Kumaran: and you would have said, Oh, back from Georgia would have went from a zone 5 to Zone 2.
251 00:47:43.890 ⇒ 00:47:49.150 Uttam Kumaran: And then you can basically estimate, oh, great! The product we ship them was a pump.
252 00:47:49.170 ⇒ 00:47:57.500 Uttam Kumaran: a zone, 2 pump is X rate. And so we would actually only paid X amount instead of what we did pay. Right? So you’re able to.
253 00:47:57.840 ⇒ 00:47:58.470 Jakob Kagel: God.
254 00:47:58.470 ⇒ 00:48:07.970 Uttam Kumaran: There’s almost like a. So there’s 1 is like, you need a table that’s zone product class. And and the price per pound.
255 00:48:08.210 ⇒ 00:48:12.670 Uttam Kumaran: or, yeah, basically, a price per pound. Right? That gives you like, the rate.
256 00:48:13.740 ⇒ 00:48:14.130 Jakob Kagel: Yeah.
257 00:48:14.190 ⇒ 00:48:16.319 Uttam Kumaran: Is a table. That’s basically.
258 00:48:16.370 ⇒ 00:48:18.380 Uttam Kumaran: where should we have ship from?
259 00:48:19.440 ⇒ 00:48:23.539 Uttam Kumaran: Once we have that, then it’s easy to say, let’s insert another one
260 00:48:23.670 ⇒ 00:48:27.210 Uttam Kumaran: like, that’s when the center of gravity helps, because you can say.
261 00:48:27.240 ⇒ 00:48:42.390 Uttam Kumaran: where does the next one need to be? And then you can basically put that in and say cool we actually had. We had all 3 of those we would have saved X amount, and had we had all 4 of these, it would be y amount. So it’s kind of like, I think. Order order up of operations. There.
262 00:48:42.850 ⇒ 00:48:52.219 Jakob Kagel: Yeah, I mean, I guess, like, so I mean, I’m just gonna use like Florida and New York as the example. Right? So how do we say like, will we just say.
263 00:48:52.540 ⇒ 00:48:59.739 Jakob Kagel: basically, like, Okay, say, we have the same product class with the same zone difference? Right?
264 00:48:59.990 ⇒ 00:49:04.150 Jakob Kagel: Or I guess it wouldn’t. But that’s the thing. I guess that’s kind of tricky, right is because, like
265 00:49:04.230 ⇒ 00:49:06.469 Jakob Kagel: something could be zoned to
266 00:49:06.490 ⇒ 00:49:16.739 Jakob Kagel: like for New York, or something like shipping right, like a zone one from Florida, right? And it’s like, How do we match and say, like.
267 00:49:17.290 ⇒ 00:49:19.520 Jakob Kagel: do we like? We just basically
268 00:49:19.700 ⇒ 00:49:23.149 Jakob Kagel: like that? That’s the part that I’m trying to think through here. Real quick is like.
269 00:49:23.150 ⇒ 00:49:37.960 Nicolas Sucari: Don’t, don’t! You need. Don’t you need there, like 2 different tables, one for each of the different warehouses, so that you can understand like, how is the distribution of what they are shipping from each of them, and then try to match both of them on, say.
270 00:49:38.220 ⇒ 00:49:46.070 Nicolas Sucari: like something that is being shipped from New York. That is Zone 5 should be shipped from Florida, and that’s like the changes that we need to do.
271 00:49:46.680 ⇒ 00:49:59.549 Nicolas Sucari: And then, understand like, which are the the places or the product classes that are being shipped on zone or or yeah, 6, 7, or 8, and understand where to place that new warehouse.
272 00:49:59.570 ⇒ 00:50:11.789 Nicolas Sucari: I I also. I’m I’m kind of under trying to understand what we need to do with the 2 different warehouses, because something for one warehouse should be some one, and probably for the other warehouse should be like a different zone. Right.
273 00:50:12.390 ⇒ 00:50:19.892 Jakob Kagel: Yeah, I wanna talk about the distance like, cause we have the field like miles from warehouse in the shipments table.
274 00:50:20.410 ⇒ 00:50:23.790 Jakob Kagel: I guess that only right? So that we only have.
275 00:50:24.070 ⇒ 00:50:39.730 Jakob Kagel: That’s from the warehouse that is shipped from. So we just need to, basically, we just need to create logic. That’s miles from the warehouse, like from from the 2 other warehouses. Right? And then we can compare and just say.
276 00:50:39.730 ⇒ 00:50:44.459 Uttam Kumaran: Share my screen. I’m just gonna do. I’ll just do an example of this in Google sheets real quick.
277 00:50:45.340 ⇒ 00:50:47.959 Uttam Kumaran: So I think it’ll help just to see it.
278 00:50:54.850 ⇒ 00:50:55.710 Uttam Kumaran: so
279 00:50:55.990 ⇒ 00:51:00.140 Uttam Kumaran: let’s say we have this shipment stable. Right? We have shipments 2, 3, 4.
280 00:51:00.300 ⇒ 00:51:01.970 Uttam Kumaran: This is a brush.
281 00:51:02.690 ⇒ 00:51:03.830 Uttam Kumaran: Seems a lot
282 00:51:04.410 ⇒ 00:51:12.560 Uttam Kumaran: right. And let’s say, customer location is GA. Tx. Ca.
283 00:51:13.200 ⇒ 00:51:14.040 Uttam Kumaran: right?
284 00:51:15.810 ⇒ 00:51:16.910 Uttam Kumaran: The
285 00:51:18.910 ⇒ 00:51:24.179 Uttam Kumaran: historical zone for this would have been like probably like 6 6
286 00:51:24.210 ⇒ 00:51:25.220 Uttam Kumaran: a.
287 00:51:25.540 ⇒ 00:51:27.899 Uttam Kumaran: And then let’s just do a brush.
288 00:51:29.320 ⇒ 00:51:32.200 Uttam Kumaran: Let’s just do a brush here that’s in
289 00:51:32.330 ⇒ 00:51:34.100 Uttam Kumaran: mass, that’s zone 2.
290 00:51:35.650 ⇒ 00:51:41.069 Uttam Kumaran: What we would have looked what there would have been there should there should be another table here that’s product
291 00:51:42.590 ⇒ 00:51:43.800 Uttam Kumaran: zone
292 00:51:44.100 ⇒ 00:51:46.329 Uttam Kumaran: and price per lb.
293 00:51:46.730 ⇒ 00:51:48.379 Uttam Kumaran: so this would be pump
294 00:51:49.440 ⇒ 00:51:51.760 Uttam Kumaran: brush. So this will be pump
295 00:51:53.420 ⇒ 00:51:54.980 Uttam Kumaran: and then brush
296 00:51:55.510 ⇒ 00:51:56.710 Uttam Kumaran: right like.
297 00:51:58.110 ⇒ 00:52:02.850 Uttam Kumaran: And this would be 1, 2, 3, 4, 5, 1, 2,
298 00:52:03.060 ⇒ 00:52:05.010 Uttam Kumaran: 3, 4, 5,
299 00:52:05.250 ⇒ 00:52:08.120 Uttam Kumaran: and let’s say it’s like 5,
300 00:52:08.430 ⇒ 00:52:09.550 Uttam Kumaran: 8,
301 00:52:09.900 ⇒ 00:52:10.910 Uttam Kumaran: and
302 00:52:10.980 ⇒ 00:52:13.509 Uttam Kumaran: well, I’ll write something like that
303 00:52:14.060 ⇒ 00:52:15.750 Uttam Kumaran: where these are
304 00:52:15.900 ⇒ 00:52:17.270 Uttam Kumaran: dollars.
305 00:52:18.700 ⇒ 00:52:22.619 Uttam Kumaran: So basically what you can do here. And then these have a wait.
306 00:52:22.770 ⇒ 00:52:30.480 Uttam Kumaran: Right? Let’s say. And then so basically, this is like, let’s say, this is like 200 pounds. This is like 5 pounds
307 00:52:30.750 ⇒ 00:52:37.519 Uttam Kumaran: 5 pounds 200 pounds. You then can do a lookup that joins to this table.
308 00:52:37.750 ⇒ 00:52:38.520 Jakob Kagel: Right.
309 00:52:38.750 ⇒ 00:52:40.180 Uttam Kumaran: On pump
310 00:52:40.470 ⇒ 00:52:42.009 Uttam Kumaran: and the zone
311 00:52:42.180 ⇒ 00:52:44.509 Uttam Kumaran: to get the shipping costs.
312 00:52:45.330 ⇒ 00:52:48.710 Uttam Kumaran: So this, this. So this ends up being what we paid.
313 00:52:50.740 ⇒ 00:52:55.480 Uttam Kumaran: So now what we hear. See, here is this is just a representation of what we already know. This is all past.
314 00:52:56.357 ⇒ 00:52:58.320 Uttam Kumaran: What we’re trying to find out
315 00:52:58.380 ⇒ 00:52:59.490 Uttam Kumaran: is
316 00:53:01.358 ⇒ 00:53:06.780 Uttam Kumaran: so the other, the other thing is, is here is historical zone, and this would have been
317 00:53:08.330 ⇒ 00:53:10.469 Uttam Kumaran: This would have been like shipped
318 00:53:11.330 ⇒ 00:53:16.549 Uttam Kumaran: from, and this would have been the Api right. This would just be the warehouses.
319 00:53:17.940 ⇒ 00:53:18.470 Jakob Kagel: Right.
320 00:53:18.470 ⇒ 00:53:28.870 Uttam Kumaran: Now, what we can do is basically say great. The reason why it’s shipped from here is because that was the only one open. But now we want to calculate a new ship from
321 00:53:31.270 ⇒ 00:53:39.739 Uttam Kumaran: ship from new, for example, and this should say, Great do a lookup between the 3 warehouses which we have warehouses.
322 00:53:40.590 ⇒ 00:53:43.556 Uttam Kumaran: warehouses, we have the yapeng.
323 00:53:44.870 ⇒ 00:53:48.199 Nicolas Sucari: You’ll need the distance from each of the warehouses to the
324 00:53:48.820 ⇒ 00:53:50.409 Nicolas Sucari: delivery place right
325 00:53:50.700 ⇒ 00:53:52.070 Nicolas Sucari: to determine.
326 00:53:52.070 ⇒ 00:53:53.729 Uttam Kumaran: So then we have, like our lat.
327 00:53:53.890 ⇒ 00:53:55.320 Uttam Kumaran: get down.
328 00:53:55.320 ⇒ 00:54:05.709 Jakob Kagel: This thing like in the table right now, I think, because even in like the distance from warehouse mapping table, like, we only have the distance based on where it actually shipped.
329 00:54:05.710 ⇒ 00:54:07.690 Uttam Kumaran: So that you have to do dynamically.
330 00:54:07.690 ⇒ 00:54:12.190 Jakob Kagel: Right? I mean, we can do this, I think right if we use like. Okay.
331 00:54:12.240 ⇒ 00:54:14.880 Jakob Kagel: if if we say for
332 00:54:15.370 ⇒ 00:54:16.170 Jakob Kagel: a
333 00:54:17.160 ⇒ 00:54:25.870 Jakob Kagel: like a shipment that shipped from like Florida. That was like a brush that was zoned to like. Compare that.
334 00:54:25.880 ⇒ 00:54:27.069 Jakob Kagel: But it’s like
335 00:54:28.090 ⇒ 00:54:35.299 Jakob Kagel: we don’t know it. Like for the specific address that we’re shipping to like. That’s the part that we have to get to.
336 00:54:37.370 ⇒ 00:54:45.970 Uttam Kumaran: I guess what I’m saying to do is create a new column that’s like shipped from new. That basically is a rank.
337 00:54:46.090 ⇒ 00:54:50.850 Uttam Kumaran: And it says, great this customer locations in Ga it.
338 00:54:50.910 ⇒ 00:54:58.470 Uttam Kumaran: there’s a join between every single customer location this table, and then there’s a there’s you calculated distance.
339 00:54:59.050 ⇒ 00:55:13.089 Uttam Kumaran: The distance is then ranked to basically say, cool this Ga. One should ship from Florida. This text one should probably ship from Florida Cai. One should ship from Ca, this one should ship from New York right? And now this is our, this is our.
340 00:55:13.180 ⇒ 00:55:20.320 Uttam Kumaran: this is our. Had we had those open, this is what it would have been. Then what we can say is cool. What is
341 00:55:20.400 ⇒ 00:55:22.480 Uttam Kumaran: this is now? The ship from?
342 00:55:23.150 ⇒ 00:55:26.210 Uttam Kumaran: We do shift from new zone.
343 00:55:26.740 ⇒ 00:55:41.680 Uttam Kumaran: Well, there’s another calculation which is basically like, figure out the zones. Now that you’re shipping from Florida. So this may have been Zone 2. This may have been Zone 2. This would have been zone one. This would have been zone 2. Then you can do this, then you join.
344 00:55:42.595 ⇒ 00:55:43.390 Uttam Kumaran: This
345 00:55:43.540 ⇒ 00:55:44.870 Uttam Kumaran: to this.
346 00:55:44.990 ⇒ 00:55:48.990 Uttam Kumaran: you join this this to this to then get the new price.
347 00:55:50.330 ⇒ 00:56:05.420 Jakob Kagel: Yeah, I mean, I get. I totally get what you’re saying. What I’m saying is like we we need like in the table is like for the given address. What’s the distance from the other warehouse like that? It didn’t ship to.
348 00:56:05.690 ⇒ 00:56:07.980 Uttam Kumaran: Yeah, so you can use this
349 00:56:09.030 ⇒ 00:56:14.319 Uttam Kumaran: you can use. I I don’t. I haven’t used this in a while. But basically you can
350 00:56:14.800 ⇒ 00:56:16.440 Uttam Kumaran: plug in the
351 00:56:16.900 ⇒ 00:56:18.549 Uttam Kumaran: the lat long here
352 00:56:19.000 ⇒ 00:56:20.140 Uttam Kumaran: and.
353 00:56:20.140 ⇒ 00:56:26.049 Jakob Kagel: For the warehouse. But then, what would we use? We would just use the like, the Zip code for the address.
354 00:56:30.300 ⇒ 00:56:32.100 Uttam Kumaran: Oh, I see!
355 00:56:32.230 ⇒ 00:56:34.086 Jakob Kagel: What I’m saying like
356 00:56:35.230 ⇒ 00:56:46.119 Jakob Kagel: because we have, like we have the distance from warehouse mapping right where we have zip code and miles from warehouse. But it’s only miles from Warehouse for the the one that is shipped from.
357 00:56:46.210 ⇒ 00:56:47.720 Jakob Kagel: you know. So it’s like
358 00:56:47.740 ⇒ 00:56:59.129 Jakob Kagel: we don’t know, basically like, okay, we ship from New York to Texas, and it was Zone 5. We don’t know if that shipment from Florida is zone 2 or zone 4.
359 00:56:59.130 ⇒ 00:56:59.923 Uttam Kumaran: I see.
360 00:57:00.320 ⇒ 00:57:11.219 Jakob Kagel: Like. I’m I’m 100 with you. I I get. I get exactly what you. What you’re saying makes perfect sense. And I just think that’s like the part that we don’t have in our data. Maybe.
361 00:57:11.220 ⇒ 00:57:17.028 Uttam Kumaran: No there. Yeah, there’s probably a function or something we basically need to do to find the
362 00:57:18.280 ⇒ 00:57:22.350 Uttam Kumaran: to find the lat long for every zip code.
363 00:57:24.650 ⇒ 00:57:25.630 Uttam Kumaran: I have.
364 00:57:25.630 ⇒ 00:57:31.700 Jakob Kagel: We have more granular even than that. I mean, we have city, of course, city State and zip so
365 00:57:32.286 ⇒ 00:57:33.440 Jakob Kagel: I mean.
366 00:57:33.610 ⇒ 00:57:36.229 Jakob Kagel: I’m not mad at us. Group like also.
367 00:57:36.230 ⇒ 00:57:37.220 Uttam Kumaran: Creating a schedule.
368 00:57:37.220 ⇒ 00:57:39.954 Jakob Kagel: Doing it more granularly than zip
369 00:57:40.630 ⇒ 00:57:41.696 Jakob Kagel: I think.
370 00:57:43.840 ⇒ 00:57:53.070 Jakob Kagel: yeah, I I just think like we should probably just append it to the table like cause. If we can calculate miles from warehouse like, if we already have the logic to do it.
371 00:57:54.210 ⇒ 00:58:10.778 Jakob Kagel: In the distance from warehouse mapping table, then we just need to create another column that’s like miles from Warehouse, Florida, miles from Warehouse, you know California. And then one that’s miles from Warehouse, Texas. And then we have all 3 like in 3 columns. And it’s like we must.
372 00:58:11.200 ⇒ 00:58:14.040 Jakob Kagel: which, because we have, it’s calculated.
373 00:58:14.210 ⇒ 00:58:23.160 Uttam Kumaran: No, I just don’t know right now. If we have a zip to lat long mapping, if not, then we can just ask Brian to build that
374 00:58:26.700 ⇒ 00:58:29.999 Uttam Kumaran: like, I don’t know whether this table has.
375 00:58:30.910 ⇒ 00:58:32.119 Uttam Kumaran: Oh, it does.
376 00:58:37.000 ⇒ 00:58:37.830 Uttam Kumaran: Yeah.
377 00:58:39.380 ⇒ 00:58:40.240 Uttam Kumaran: cool.
378 00:58:42.250 ⇒ 00:58:44.199 Uttam Kumaran: So you can join on zip
379 00:58:44.840 ⇒ 00:58:47.670 Uttam Kumaran: to this table and basically get the lat.
380 00:58:47.880 ⇒ 00:58:52.800 Jakob Kagel: Which like. What’s this schema under like? Which table is this under.
381 00:58:54.140 ⇒ 00:58:55.539 Uttam Kumaran: It’s under
382 00:58:56.020 ⇒ 00:58:59.980 Uttam Kumaran: sip demographics, although I think this should be in Dbt. Here.
383 00:58:59.980 ⇒ 00:59:00.980 Jakob Kagel: Randomly.
384 00:59:04.120 ⇒ 00:59:06.149 Jakob Kagel: I see it us Zip code Medic.
385 00:59:06.150 ⇒ 00:59:13.110 Uttam Kumaran: Yeah. So so, so if you look under Dbt reference, there’s a Zip code metadata table here to same thing. We just
386 00:59:13.170 ⇒ 00:59:16.270 Uttam Kumaran: we just copied it over, cause it doesn’t change.
387 00:59:16.670 ⇒ 00:59:17.069 Jakob Kagel: Okay.
388 00:59:17.470 ⇒ 00:59:20.780 Uttam Kumaran: So use this one. And this one has
389 00:59:22.180 ⇒ 00:59:25.019 Uttam Kumaran: this. One has a lot long for every city.
390 00:59:25.940 ⇒ 00:59:27.950 Uttam Kumaran: for every. So for every zip.
391 00:59:28.290 ⇒ 00:59:31.809 Uttam Kumaran: So you actually can do it on Zip, I guess. Basically, you would.
392 00:59:32.140 ⇒ 00:59:38.599 Uttam Kumaran: Yeah, you have to join every order on Zip to get the lat long, and then you plug the lat. Long into whatever the Geo distance
393 00:59:38.960 ⇒ 00:59:42.056 Uttam Kumaran: function is, and let me where where is that?
394 00:59:42.400 ⇒ 00:59:51.810 Jakob Kagel: Saying, I mean, it makes sense like we can do it. But I’m just wondering like, Can we just not like append it like we already have the logic for miles from warehouse like, can we not just create.
395 00:59:51.810 ⇒ 00:59:52.910 Uttam Kumaran: Where is that?
396 00:59:53.520 ⇒ 00:59:54.150 Jakob Kagel: In
397 00:59:54.790 ⇒ 01:00:02.470 Jakob Kagel: It’s in the. It’s in both. I think it’s in shipments, and it’s in the Dbt. Like distance from warehouse table.
398 01:00:02.470 ⇒ 01:00:05.690 Uttam Kumaran: Oh, zip! Code! Distance from warehouse mapping.
399 01:00:05.690 ⇒ 01:00:11.413 Jakob Kagel: Right. So we already have the logic to do that. I think it’s maybe easier if we
400 01:00:12.210 ⇒ 01:00:17.180 Jakob Kagel: if we just add like 2 more columns is, you have the logic right here? Is this it? Right?
401 01:00:17.370 ⇒ 01:00:23.540 Uttam Kumaran: Yeah, so this is, what is this doing? So this is for every warehouse location.
402 01:00:35.380 ⇒ 01:00:36.050 Jakob Kagel: And that’s the.
403 01:00:36.460 ⇒ 01:00:36.870 Uttam Kumaran: Up.
404 01:00:36.870 ⇒ 01:00:37.420 Jakob Kagel: He’s.
405 01:00:37.420 ⇒ 01:00:41.409 Uttam Kumaran: This is, let’s see, the problem with this is this is every zip.
406 01:00:41.750 ⇒ 01:00:42.800 Uttam Kumaran: This is
407 01:00:43.020 ⇒ 01:00:46.509 Uttam Kumaran: the distance of every zip from just the New York. One
408 01:00:46.690 ⇒ 01:00:48.530 Uttam Kumaran: cause. This is the New York set.
409 01:00:48.780 ⇒ 01:00:50.320 Jakob Kagel: Oh, okay.
410 01:00:50.320 ⇒ 01:00:52.930 Uttam Kumaran: But what he’s doing, what he’s doing is basically
411 01:00:53.040 ⇒ 01:00:58.889 Uttam Kumaran: joining them together to basically find out every zips distance from this sip.
412 01:01:00.606 ⇒ 01:01:03.679 Jakob Kagel: But we could do the same thing for the other zips, like.
413 01:01:03.680 ⇒ 01:01:07.610 Uttam Kumaran: Yeah, basically, you probably want every zips. But
414 01:01:08.230 ⇒ 01:01:13.579 Uttam Kumaran: this is the thing. It’s kind of a big table, cause you want every zips location from
415 01:01:13.600 ⇒ 01:01:15.619 Uttam Kumaran: all 4 warehouses.
416 01:01:16.040 ⇒ 01:01:21.809 Jakob Kagel: Yeah, but it’s not the tables, not like you’re you’re not adding any more rows like you’re just adding columns like.
417 01:01:21.810 ⇒ 01:01:24.899 Uttam Kumaran: Yeah. So you want every zips.
418 01:01:26.030 ⇒ 01:01:34.460 Jakob Kagel: It’s not every I mean, yeah, I mean, it’s like, it’s every zip. But it’s the same logic that we just use here. You just swap out the the Zip code like instead.
419 01:01:34.460 ⇒ 01:01:37.399 Uttam Kumaran: Yeah, I I just I just think it’s not like.
420 01:01:38.510 ⇒ 01:01:39.970 Uttam Kumaran: I guess.
421 01:01:45.950 ⇒ 01:01:46.830 Uttam Kumaran: yeah.
422 01:01:47.250 ⇒ 01:01:50.202 Jakob Kagel: I mean, I’m otherwise. I’m probably gonna just do the same thing like.
423 01:01:50.430 ⇒ 01:01:54.980 Uttam Kumaran: No, no, no, I’m just thinking about how to handle it. For if you want to do this stuff for Texas.
424 01:01:55.329 ⇒ 01:02:05.840 Uttam Kumaran: like the new location, basically. So I guess, okay, so that’s fine. So I think, I think, yeah, we should. I think. Pro honestly, you should probably just take this out.
425 01:02:06.080 ⇒ 01:02:08.029 Uttam Kumaran: use it in a cte.
426 01:02:08.140 ⇒ 01:02:09.280 Uttam Kumaran: yeah. And then.
427 01:02:09.610 ⇒ 01:02:12.339 Uttam Kumaran: whatever modifications we need to do to this.
428 01:02:12.630 ⇒ 01:02:13.400 Jakob Kagel: Okay.
429 01:02:13.400 ⇒ 01:02:14.588 Uttam Kumaran: We do it?
430 01:02:15.380 ⇒ 01:02:21.650 Jakob Kagel: Can you just send me the the link? Just put in the chat real quick, or just put in slack, is maybe.
431 01:02:22.010 ⇒ 01:02:22.660 Uttam Kumaran: Yeah.
432 01:02:23.460 ⇒ 01:02:29.216 Jakob Kagel: That’s fine. I mean, that works for me. I mean, I could do it faster that way, too.
433 01:02:29.520 ⇒ 01:02:33.699 Uttam Kumaran: Otherwise it’s gonna be a back and forth, and it might. It might take a day or 2. So.
434 01:02:33.920 ⇒ 01:02:34.483 Jakob Kagel: Okay.
435 01:02:35.610 ⇒ 01:02:43.110 Uttam Kumaran: But yeah, I I mean, basically this, that’s all this is doing. And then in terms of figuring out where the next. So this will give you.
436 01:02:44.250 ⇒ 01:02:50.579 Uttam Kumaran: Okay. So maybe we just start there. Basically figure out if we had these 3 and we optimized for distance
437 01:02:51.180 ⇒ 01:02:53.059 Uttam Kumaran: what the estimated
438 01:02:54.210 ⇒ 01:02:58.459 Uttam Kumaran: like shipping savings would have been. And then it’s basically saying.
439 01:02:59.310 ⇒ 01:03:03.179 Uttam Kumaran: using that if we were to add a 4, th where should it be.
440 01:03:04.870 ⇒ 01:03:15.510 Jakob Kagel: Right? Because I guess we’re honestly, we’re kind of answering 2 questions, right? Which is like, one is like, Okay, how many orders can we actually like ship out of Florida that are going to Texas right.
441 01:03:15.510 ⇒ 01:03:16.979 Uttam Kumaran: Yeah. Man.
442 01:03:17.160 ⇒ 01:03:21.240 Jakob Kagel: Like, how does that kind of like affect it, too? Because
443 01:03:21.330 ⇒ 01:03:37.830 Jakob Kagel: in theory, I mean, I don’t know. I’m just hypothesizing. But if Florida, like you can ship more to East Texas, you know. That maybe would change the story. I mean, you’re shipping probably more to East Texas, too, from New York. I mean, they’re both on the east coast. But
444 01:03:38.260 ⇒ 01:03:42.850 Jakob Kagel: it seems like, yeah, West, Texas would be sort of the one. But
445 01:03:42.920 ⇒ 01:03:44.520 Jakob Kagel: yeah, okay.
446 01:03:45.570 ⇒ 01:03:50.570 Uttam Kumaran: Yeah, cause we’re trying. So we’re trying. We just we want. We’re trying to just prove we’re trying to do 2 things, one
447 01:03:50.580 ⇒ 01:03:58.180 Uttam Kumaran: like we’re trying to prove the impact. And then, second, we’re trying to optimize, for where? So we need to know.
448 01:03:58.490 ⇒ 01:04:11.460 Uttam Kumaran: we just need to know all the where all the existing shipments would have shipped from, and then, because otherwise, if we do it, if we just look at Texas now, because the other ones aren’t like fully ramped up.
449 01:04:11.500 ⇒ 01:04:17.370 Uttam Kumaran: It’s not going to be a good way of saying you’re going to steal from Florida when you should have shipped from Florida. For example.
450 01:04:17.560 ⇒ 01:04:23.610 Uttam Kumaran: we need basically a situation where we have, we have a scenario where we have all 4 online.
451 01:04:23.620 ⇒ 01:04:31.859 Uttam Kumaran: And they’re like fully optimized meaning basically every shipment that should ship from one of them is getting shipped from there.
452 01:04:31.880 ⇒ 01:04:36.180 Uttam Kumaran: And then we can basically say, cool to get the lowest price. Where should that be?
453 01:04:37.038 ⇒ 01:04:41.490 Uttam Kumaran: That thing I don’t. I’m not really like. I’m not sure exactly like how.
454 01:04:41.880 ⇒ 01:05:11.869 Jakob Kagel: I’m thinking it’s like, if we have a short list like cause, I mean, okay, like, if we do the center of gravity, right? It’ll give us like the lat long and like, you know, whatever it might end up. You know the lat long might be, whatever some rural town or something. And then we have to pick like the the one that’s like closest to it. But list of like, okay, like, I mean, I can even do this now, like while we’re using the zip codes right? It’s like, I can say, like, What’s the Zip code for Dallas? What’s the Zip code for San Antonio, like, you know, but I don’t know if we want.
455 01:05:11.870 ⇒ 01:05:17.060 Uttam Kumaran: Oh, I see you basically. Well, that. So that’s the thing is, basically I was I you could go either way.
456 01:05:17.556 ⇒ 01:05:20.680 Uttam Kumaran: Ask? You can ask them initially where?
457 01:05:20.950 ⇒ 01:05:31.910 Uttam Kumaran: Yeah, you could ask them, hey, give me all the give me all your warehouses, and then we plug them in and find it. Or we basically find we find the ideal literally zip.
458 01:05:31.940 ⇒ 01:05:34.800 Uttam Kumaran: And then we basically say anything closest to this.
459 01:05:35.240 ⇒ 01:05:55.322 Jakob Kagel: Right. I think we should do both. I mean, I think. I I think it if we get like the list of like all the warehouses, and then we can just do it exactly like how you’re describing right here, where it’s like we just take the distance. And, like, you know we can group by zone and all that, but then it’s like good, because we have like a short list we can anchor on, you know. I mean.
460 01:05:55.570 ⇒ 01:05:57.019 Uttam Kumaran: Okay, I’m going.
461 01:05:57.170 ⇒ 01:05:59.729 Jakob Kagel: Maybe they have like a hundred or something, I mean.
462 01:05:59.730 ⇒ 01:06:03.119 Uttam Kumaran: That’s what I don’t know. I’m gonna I’ll just email him and say, Hey, can you?
463 01:06:03.750 ⇒ 01:06:05.209 Uttam Kumaran: Can you send us
464 01:06:06.110 ⇒ 01:06:11.370 Uttam Kumaran: all the shipmates in like the Texas area or all the warehouses you guys have.
465 01:06:11.600 ⇒ 01:06:14.156 Jakob Kagel: Right? I mean, that would be the best thing. Yeah, I think.
466 01:06:14.370 ⇒ 01:06:18.840 Uttam Kumaran: Start there. If it’s a if I’m basically gonna say, if if this is, if it’s too much.
467 01:06:18.900 ⇒ 01:06:21.480 Uttam Kumaran: then we’ll still do what we’re gonna do.
468 01:06:23.090 ⇒ 01:06:32.279 Jakob Kagel: I think it’s good if we work both angles. But yeah, I think it is helpful, like, I think that would be helpful. And
469 01:06:33.440 ⇒ 01:06:51.499 Jakob Kagel: yeah, especially like, I don’t know if they if we can get it to like a short list like if it is a lot, if we can get it to a short list, you know. Somehow I don’t know if it’s like based on, you know their capacity, or like the warehouse capacity, or whatever. But I don’t know. Yeah, I think that would be helpful.
470 01:06:51.500 ⇒ 01:06:58.010 Uttam Kumaran: I mean, I’ll just pose a question, I said. Can he? He already sent our the one in Roanoke? I don’t know where that is, I can basically say, Can you give us?
471 01:06:58.200 ⇒ 01:07:00.149 Uttam Kumaran: Can you give us other options
472 01:07:01.140 ⇒ 01:07:03.310 Uttam Kumaran: like in the surrounding.
473 01:07:03.900 ⇒ 01:07:04.926 Jakob Kagel: That’s great. Yeah,
474 01:07:05.970 ⇒ 01:07:06.330 Uttam Kumaran: There!
475 01:07:06.670 ⇒ 01:07:11.339 Jakob Kagel: Helpful to like what you shared here, because, like now, I know, like the logic for the zone.
476 01:07:11.340 ⇒ 01:07:12.453 Uttam Kumaran: Yeah, yeah.
477 01:07:13.010 ⇒ 01:07:13.979 Jakob Kagel: That too.
478 01:07:13.980 ⇒ 01:07:20.640 Uttam Kumaran: And and this is, and this is the this is the real. This is the actual like key thing. Out of everything is
479 01:07:21.704 ⇒ 01:07:26.109 Uttam Kumaran: in snowflake. You create like a make point, which is just like a object.
480 01:07:26.240 ⇒ 01:07:29.120 Uttam Kumaran: So he creates 2 make points, one for
481 01:07:29.370 ⇒ 01:07:31.779 Uttam Kumaran: the lat long of where you’re
482 01:07:31.870 ⇒ 01:07:36.620 Uttam Kumaran: of where you’re thinking of shipping to the lat. Long of the warehouse you’re shipping from.
483 01:07:36.920 ⇒ 01:07:41.390 Uttam Kumaran: and then gets the distance and then divides it by 16 0 9, cause I don’t.
484 01:07:41.630 ⇒ 01:07:44.080 Uttam Kumaran: 16 9 is, maybe this comes in feet.
485 01:07:45.240 ⇒ 01:07:57.429 Uttam Kumaran: maybe converting it to Miles. You don’t really need to do the conversion because it doesn’t really matter. Oh, I guess. Oh, it matters because for these zones. So yeah, this conversion matters. And then this is the
486 01:07:58.520 ⇒ 01:07:59.640 Uttam Kumaran: thing. Yeah.
487 01:08:00.020 ⇒ 01:08:00.800 Jakob Kagel: Okay.
488 01:08:00.800 ⇒ 01:08:04.220 Uttam Kumaran: So I would just, yeah, I would just use this in the cte, and then
489 01:08:04.310 ⇒ 01:08:07.060 Uttam Kumaran: keep making modifications. This, which is basically
490 01:08:07.100 ⇒ 01:08:09.400 Uttam Kumaran: probably adding
491 01:08:09.590 ⇒ 01:08:13.160 Uttam Kumaran: list to this to basically get every zips distance from
492 01:08:13.180 ⇒ 01:08:16.990 Uttam Kumaran: the 4 warehouses or the 3 warehouses currently.
493 01:08:17.880 ⇒ 01:08:22.450 Uttam Kumaran: And then basically, you can say, pick the one with the least distance.
494 01:08:22.840 ⇒ 01:08:28.763 Jakob Kagel: For sure. Exactly. It’s like the proof of of concept or whatever like you were saying, like, Yeah, we can.
495 01:08:29.069 ⇒ 01:08:30.142 Uttam Kumaran: At this? Yeah.
496 01:08:30.830 ⇒ 01:08:40.419 Jakob Kagel: The thing is like. They probably already know that. And they that’s why they already want to do the the warehouse. So that’s why, it’s like really good. If we can sort of get the list of the other.
497 01:08:40.420 ⇒ 01:08:41.140 Uttam Kumaran: Yeah.
498 01:08:41.660 ⇒ 01:08:46.369 Uttam Kumaran: Well, cause the the thing is is like, I think we just said Cali. And I think we just said
499 01:08:46.560 ⇒ 01:08:53.450 Uttam Kumaran: Texas, because I just guessed. So instead, I’m like, I want us to know that this is the right decision.
500 01:08:53.640 ⇒ 01:08:57.389 Uttam Kumaran: because maybe it’s further north. Or maybe it’s even further south. I don’t know.
501 01:08:57.620 ⇒ 01:08:58.350 Jakob Kagel: Laugh.
502 01:08:58.620 ⇒ 01:09:04.205 Jakob Kagel: Okay, that sounds good. Alright. Well, I think, yeah, I think we’re yeah. We’re on the right track now.
503 01:09:04.750 ⇒ 01:09:06.390 Uttam Kumaran: Okay, I’m just gonna share this.
504 01:09:07.210 ⇒ 01:09:08.183 Uttam Kumaran: Okay, cool.
505 01:09:09.620 ⇒ 01:09:20.989 Jakob Kagel: Sounds good. I mean, I think, yeah, that’s good. I mean, we definitely like, if we need to provide an update, I think we should just yeah, explain basically that we’re doing like, you know, we have to do this mapping that we didn’t have. And yeah.
506 01:09:21.630 ⇒ 01:09:31.359 Uttam Kumaran: No, I think this is fine. I mean, I think the again. The biggest thing is I want to run in order. I want to run everything through chuck and then through Ben, and then and then send it out.
507 01:09:31.399 ⇒ 01:09:35.799 Uttam Kumaran: So whenever you get to a place where you have a hypothesis on this
508 01:09:36.109 ⇒ 01:09:38.979 Uttam Kumaran: just like shooting shipments. Yeah.
509 01:09:39.640 ⇒ 01:09:45.890 Jakob Kagel: That sounds good. Okay, cool. Well, yeah, I’ll keep. I’ll keep going. I’ll keep going on it. And yeah.
510 01:09:45.890 ⇒ 01:09:48.710 Uttam Kumaran: Yeah, I think this will be a good table to have, too, which is.
511 01:09:49.800 ⇒ 01:09:53.760 Uttam Kumaran: yeah, this will be probably Ct or something which is just like product zone.
512 01:09:53.770 ⇒ 01:09:57.240 Uttam Kumaran: and the price per pound. So this is this is like a historical
513 01:09:57.690 ⇒ 01:10:02.190 Uttam Kumaran: his. This is like a historical price, like a rate sheet for that. We’ve seen.
514 01:10:02.570 ⇒ 01:10:03.340 Jakob Kagel: Right.
515 01:10:03.340 ⇒ 01:10:06.099 Uttam Kumaran: Again, there’s so many factors right? Like, I don’t know whether.
516 01:10:06.860 ⇒ 01:10:11.689 Uttam Kumaran: like, yeah, there’s just so many factors that the the less we can like kind of like deal with.
517 01:10:12.140 ⇒ 01:10:14.750 Uttam Kumaran: We’ll just. We’ll just talk about zones, and then
518 01:10:14.760 ⇒ 01:10:19.839 Uttam Kumaran: we’ll take this like price per pound, because up all these guys have this as a rate sheet.
519 01:10:19.850 ⇒ 01:10:23.949 Uttam Kumaran: But it’s going with our historicals is, gonna be a good measure.
520 01:10:25.220 ⇒ 01:10:28.529 Uttam Kumaran: And then we don’t have to think about really the weight or anything.
521 01:10:28.730 ⇒ 01:10:31.520 Uttam Kumaran: because we’re looking at it by product class.
522 01:10:31.740 ⇒ 01:10:36.380 Uttam Kumaran: Well, we don’t. We don’t have to look at like who the provider is. We just take the averages.
523 01:10:36.650 ⇒ 01:10:37.400 Jakob Kagel: Yeah.
524 01:10:37.730 ⇒ 01:10:38.290 Uttam Kumaran: Yeah.
525 01:10:38.620 ⇒ 01:10:46.170 Jakob Kagel: That makes sense. Okay? Great, awesome. Well, yeah. Unless there’s anything else. Then yeah, I’ll just. I’ll I’ll get to it. I’ll keep you posted.
526 01:10:46.170 ⇒ 01:10:50.689 Uttam Kumaran: Okay? And I’m I told actually to just hit you for any feedback on evidence stuff. So.
527 01:10:50.690 ⇒ 01:11:13.050 Jakob Kagel: For sure. I saw actually, I saw you validated. The sales numbers. So that’s great. They look good to me. Let’s maybe touch base tomorrow. And yeah, actually to like, just feel free. If if you don’t mind just shoot me over the evidence, page, and I’ll try and take a look at it today, and then we can maybe walk through it tomorrow.
528 01:11:15.040 ⇒ 01:11:17.170 Akshay kumar.G: Yeah, do you want to share the screen? Now.
529 01:11:18.270 ⇒ 01:11:19.210 Akshay kumar.G: Kirby.
530 01:11:19.760 ⇒ 01:11:22.549 Jakob Kagel: We can go through. And now, if you want Tom, it’s up to you, honest.
531 01:11:22.550 ⇒ 01:11:23.870 Uttam Kumaran: I have to jump. Yeah.
532 01:11:23.870 ⇒ 01:11:24.480 Akshay kumar.G: Hadn’t.
533 01:11:25.870 ⇒ 01:11:34.640 Jakob Kagel: No worries. Yeah, let’s if it’s okay with you, actually, let’s let’s sync up tomorrow. I’ll review it with you, and then if it looks good we’ll run it by the Tom.
534 01:11:36.490 ⇒ 01:11:37.270 Akshay kumar.G: Yes, sir.
535 01:11:37.630 ⇒ 01:11:39.689 Jakob Kagel: That’s great. Thanks. Appreciate.
536 01:11:40.100 ⇒ 01:11:41.430 Uttam Kumaran: Okay, thanks guys.
537 01:11:43.350 ⇒ 01:11:44.090 Nicolas Sucari: And I.