Meeting Title: Uttam-Kumaran’s-Personal-Meeting-Room Date: 2023-11-08 Meeting participants: Bencohen, Uttam Kumaran
WEBVTT
1 00:00:31.120 ⇒ 00:00:32.130 Uttam Kumaran: Hey, Ben?
2 00:00:32.470 ⇒ 00:00:34.569 Okay, how’s it going?
3 00:00:34.660 ⇒ 00:00:38.580 Uttam Kumaran: Good. How are you? Give me one. Sec. Let me. Just yeah.
4 00:00:40.450 ⇒ 00:00:42.789 Uttam Kumaran: How was, how was stuff with Kim
5 00:00:44.180 ⇒ 00:00:50.890 bencohen: Umhm! How was everything with Kim? Oh, good fun! Day
6 00:00:52.050 ⇒ 00:00:53.260 bencohen: Sunday.
7 00:00:54.840 ⇒ 00:00:56.240 bencohen: What’s up with you?
8 00:00:57.020 ⇒ 00:01:06.220 Uttam Kumaran: Stuff’s good. It’s just getting a little bit colder here in Austin, but I don’t know. Everything else is pretty good. We’ve been.
9 00:01:06.410 ⇒ 00:01:20.139 Uttam Kumaran: There’s a lot I gotta show you today that I think we’ll have a bunch of directions to kind of go from. And then, yeah, I’m like, I’m in between speaking with the next. Folks today about like cutting that off.
10 00:01:20.300 ⇒ 00:01:26.139 bencohen: which has been like it’s been the worst like I can’t. I’ve been asking them like, Can you just send me
11 00:01:26.150 ⇒ 00:01:30.630 Uttam Kumaran: the invoices so I can understand. And they’re like. Oh.
12 00:01:30.820 ⇒ 00:01:34.989 Uttam Kumaran: like, what do you want? And I’m like the invoices, and they’re like, Oh.
13 00:01:35.090 ⇒ 00:01:37.190 Uttam Kumaran: you have to go through finance. I’m like.
14 00:01:37.880 ⇒ 00:01:45.869 bencohen: I don’t care who I have to go through. Just get me the answer, I mean
15 00:01:47.080 ⇒ 00:02:02.570 bencohen: II it was tough because it was hard to decipher between. Was nexl not great, or was Praveen not great, I mean, I think. Like, I just think he yeah, I just think he spent
16 00:02:02.630 ⇒ 00:02:14.530 Uttam Kumaran: too much time, probably on like bringing the data in. which is like a pretty solved problem, like, there’s some vendor that will get you the data from another vendor.
17 00:02:14.580 ⇒ 00:02:16.080 Uttam Kumaran: And so
18 00:02:16.910 ⇒ 00:02:25.260 Uttam Kumaran: the best thing is just to find whoever’s established in doing that and just go with them. And it’s like the stuff in Nexa is like very very custom.
19 00:02:25.590 ⇒ 00:02:31.090 Uttam Kumaran: and what that means is like, if I have to go make a change. It’s like it takes like hours.
20 00:02:31.130 ⇒ 00:02:44.089 Uttam Kumaran: So I was like, okay, let’s just let’s just kinda like, rip them out, and then, once we can, I get established we’ll start to move stuff over. So Amazon will kind of be next the ship station stuff. We have a ton more
21 00:02:44.160 ⇒ 00:02:50.320 Uttam Kumaran: data that I’ll show you. We have pretty much all the data now at like the shipment
22 00:02:50.370 ⇒ 00:02:56.069 Uttam Kumaran: and the shipment item level, which is like the weight, the dimensions, everything. So
23 00:02:56.930 ⇒ 00:02:58.300 Uttam Kumaran: it’s a lot better.
24 00:02:58.510 ⇒ 00:03:06.479 bencohen: So do you think we can have it in a situation where in order comes in and ship station decides the most efficient
25 00:03:06.960 ⇒ 00:03:25.090 Uttam Kumaran: option automatically. So there’s no manual intervention. Yeah. So that’s what I’m I’m gonna go through some of this stuff. They the ship station folks send me today. I’ll show you kind of like what I have right now. The the thing is, Chuck was like, look, we. We send so many that like there, it’s probably not
26 00:03:25.130 ⇒ 00:03:27.399 Uttam Kumaran: easy to just do that. But
27 00:03:27.430 ⇒ 00:03:38.810 Uttam Kumaran: one thing that, like I’ll kind of show you walk you just through at least the the entire flow. So, for example, there’s this item here in ship station.
28 00:03:38.910 ⇒ 00:03:55.110 Uttam Kumaran: the ladder all the dimension all the way. Information. What happens in this situation is that from ups they give us like a rate which is either the weight and the zone, or they multiply the length, width, and height, and divided by
29 00:03:55.170 ⇒ 00:04:06.440 Uttam Kumaran: like a discount factor, which is for us, it’s like 1, 66. What I what pretty much we’ll have to go and negotiate with is either better zone to weight pricing, or a better.
30 00:04:06.620 ⇒ 00:04:17.560 Uttam Kumaran: like sizing, pricing, meaning. It’ll be like length times with times height divided by 200 or something like that, and they’ll and they’ll they’ll pick. They’ll pick the highest one
31 00:04:17.670 ⇒ 00:04:28.919 Uttam Kumaran: So, for example, like we have this item here, all the information, and similarly, in light dash, we now could pretty much go filter by this individual order Id.
32 00:04:29.130 ⇒ 00:04:36.839 Uttam Kumaran: and have pretty much all the same information. So the product order. Id the platform it sold on the Zone. It’s going to
33 00:04:36.880 ⇒ 00:04:47.420 Uttam Kumaran: the platform, the shipping service, and then, as well as all of the different like cost measures. So the price, the quantity, the discount
34 00:04:47.500 ⇒ 00:04:54.860 Uttam Kumaran: weight shipment costs all the way down to the unleashed cost. and then finally to like what we earned.
35 00:04:54.890 ⇒ 00:04:59.749 Uttam Kumaran: So we have like from my mind. We almost have everything here.
36 00:05:00.190 ⇒ 00:05:05.590 Uttam Kumaran: like attached to an individual order. Of course, am I showing any of the customer information or the
37 00:05:05.820 ⇒ 00:05:07.709 Uttam Kumaran: the location stuff. But
38 00:05:08.170 ⇒ 00:05:19.080 Uttam Kumaran: I think this is kind of like at the individual order item level. We now have, like all the information. So this is where I’m rolling everything up from
39 00:05:19.880 ⇒ 00:05:25.499 Uttam Kumaran: So just wanted to show that. And then the other things that we’ve been really
40 00:05:25.560 ⇒ 00:05:27.830 Uttam Kumaran: working on
41 00:05:28.370 ⇒ 00:05:44.050 Uttam Kumaran: one is like we kind of, we’re rolling a bit off next led. And we’re also bringing in a lot of the data from the Google sheets. So all of the stuff that chuck shared, which is like when the supply comes in. Yeah, and so we kind of now
42 00:05:44.570 ⇒ 00:05:51.709 Uttam Kumaran: have, like a pretty good sense of like overall sales across like Skus. And then, right now, we’re working on.
43 00:05:52.560 ⇒ 00:05:59.390 Uttam Kumaran: One of the things we’re working on is like more of the supply chain. So this is like taking everything from this
44 00:05:59.420 ⇒ 00:06:10.890 Uttam Kumaran: spreadsheet, the Bd via our spreadsheet. The only thing I was gonna ask is whether we could transition to a new
45 00:06:11.180 ⇒ 00:06:12.400 Uttam Kumaran: format.
46 00:06:12.530 ⇒ 00:06:37.219 bencohen: Yeah, that’s that’s this isn’t made for you. You know what I mean. What? Yeah. And this is like, this is pretty much like what I’m proposing, which is just like it’s just in a column format. And yeah, so do for our purposes is different. Obviously, hopefully, we don’t make manual spreadsheets so much anymore when we have the
47 00:06:37.310 ⇒ 00:06:42.020 bencohen: dashboard to do it all for us, you know. So whatever you want to do.
48 00:06:42.100 ⇒ 00:06:54.250 Uttam Kumaran: okay, cool. So right now, I’m bringing in this data, which is all when this is from the same spreadsheet, just for these skews, for when supplies coming in and we’re actually now able to see
49 00:06:54.540 ⇒ 00:07:16.219 Uttam Kumaran: pretty much when all the shipments arrive. And then what we’re gonna start to do is also look at turnover which we’ll probably end up doing today or tomorrow, which is like, when does the shipment arrive and pairing that with the actual sales? So we’ll be able to see? Okay, there’s like a certain amount of shipment here. And how does that get drawn down over time
50 00:07:16.690 ⇒ 00:07:19.950 Uttam Kumaran: and kind of looking at each skew and looking at.
51 00:07:20.470 ⇒ 00:07:28.400 Uttam Kumaran: How long does this pretty much stay on the shelf? So that’s like the next thing we’re working on when it comes to inventory.
52 00:07:28.500 ⇒ 00:07:29.350 Uttam Kumaran: But
53 00:07:29.660 ⇒ 00:07:37.549 Uttam Kumaran: this dashboard, I think, is probably the best place for everything inventory-wise, which just has, like all the orders today
54 00:07:37.640 ⇒ 00:07:43.990 Uttam Kumaran: as well as everything at a ski level. Which is like
55 00:07:44.160 ⇒ 00:07:49.190 Uttam Kumaran: the sku. How many was ordered this month, this month, last year.
56 00:07:49.310 ⇒ 00:07:52.660 Uttam Kumaran: and in this month, 3 months ago, and 6 months ago.
57 00:07:52.930 ⇒ 00:07:56.639 bencohen: So I was gonna ask you whether this was like
58 00:07:57.420 ⇒ 00:08:01.400 Uttam Kumaran: what else we would need to put in like an inventory, related Dashboard.
59 00:08:01.450 ⇒ 00:08:05.180 bencohen: That might be helpful.
60 00:08:06.840 ⇒ 00:08:18.399 Uttam Kumaran: So right now, I just have like, here’s today’s orders. Here, here are like, this is just a sales, and I’ll kind of walk through like some of some some of the forecast stuff we’re doing. This is like when shipments arrive.
61 00:08:18.590 ⇒ 00:08:32.370 Uttam Kumaran: and then this is just the actual skew level sales. but I don’t know from the operational side what’s helpful to look at from inventory, turnover, and things like that.
62 00:08:34.400 ⇒ 00:08:37.179 bencohen: I’ll have to think about it. I mean, this is pretty.
63 00:08:37.330 ⇒ 00:08:46.230 bencohen: This is, you know, this has all the stuff. I think you have sorting options. You can can sort a set and descend from the top or no. Yeah, yeah, you can do that. Yeah.
64 00:08:47.540 ⇒ 00:08:57.509 bencohen: nice. Yeah, let’s just start. I don’t want to get. Let’s just let’s let’s start simple. Use it and then go from there. I don’t want to over
65 00:08:57.980 ⇒ 00:09:00.660 bencohen: project and over-engineer in my mind.
66 00:09:00.780 ⇒ 00:09:06.069 Uttam Kumaran: Okay. the other thing kind of started doing is on the shipping side.
67 00:09:06.100 ⇒ 00:09:10.970 Uttam Kumaran: So this is actually our zone by zone and month shipping.
68 00:09:12.350 ⇒ 00:09:16.419 Uttam Kumaran: So you can see which months and which zones zones actually take up
69 00:09:16.810 ⇒ 00:09:19.009 Uttam Kumaran: like a huge amount of spend free.
70 00:09:19.290 ⇒ 00:09:20.660 Uttam Kumaran: for example, like
71 00:09:20.890 ⇒ 00:09:28.670 Uttam Kumaran: July zone 2, you can see, and then you can kind of see as we go towards like the further zones.
72 00:09:28.770 ⇒ 00:09:31.130 Uttam Kumaran: although there may be like lower quantity.
73 00:09:31.540 ⇒ 00:09:39.470 Uttam Kumaran: It’s like super expensive to ship to like zone 7 and zone 8 killers.
74 00:09:40.400 ⇒ 00:09:41.750 Uttam Kumaran: So.
75 00:09:42.000 ⇒ 00:09:48.729 Uttam Kumaran: and then also on this, that of this thing you can see by skew. You can actually see the total shipment cost.
76 00:09:50.320 ⇒ 00:09:54.490 bencohen: as well as what were quoted by Ups and Fedex.
77 00:09:55.490 ⇒ 00:09:57.260 Uttam Kumaran: based on their rate, cards
78 00:09:57.470 ⇒ 00:10:01.600 bencohen: quoted, and paying, do those end up being the same thing?
79 00:10:01.640 ⇒ 00:10:11.169 bencohen: No, in some situations they’re close in some situations. They’re further, can we? That’s something that I’d be curious about.
80 00:10:11.190 ⇒ 00:10:13.270 bencohen: having more visibility on.
81 00:10:13.390 ⇒ 00:10:14.590 Uttam Kumaran: Yeah.
82 00:10:15.150 ⇒ 00:10:25.980 bencohen: You assume, by the way, that I assume they’re always closed, which is a mistake to assume. I know. Yeah. So that’s so, that’s what I was gonna ask you to is
83 00:10:26.970 ⇒ 00:10:40.409 Uttam Kumaran: about kind of the strategy for the negotiation. The reason why we have the Ups rate and the Fedex rate is. So I can actually do that analysis which is like, what’s the gap? The problem is, there’s so many skews, and there’s there’s a lot of ways to kind of
84 00:10:41.170 ⇒ 00:10:59.249 Uttam Kumaran: try to press them. Wh is there? Do you have an idea of like a couple of skews to try to attack, or a couple of areas. Maybe I can ask them, because again, we have so many zones, we have a ton of skews, and they change over months. I wanna be like what’s like a key area
85 00:10:59.510 ⇒ 00:11:04.630 Uttam Kumaran: that I can try to go after to say like, let’s see if we can reduce the cost of these 3 Skus.
86 00:11:04.790 ⇒ 00:11:07.580 bencohen: and then I can kind of put something together.
87 00:11:07.780 ⇒ 00:11:15.310 bencohen: Just text him. After. That’ll be easy. II know roughly, but he knows exactly.
88 00:11:15.560 ⇒ 00:11:16.710 Okay.
89 00:11:17.440 ⇒ 00:11:18.959 bencohen: that’ll be a quick one.
90 00:11:20.300 ⇒ 00:11:27.550 Yeah. So so the one thing that’s great is that from both of their rate sheets we have all the data in. And so
91 00:11:27.710 ⇒ 00:11:40.459 Uttam Kumaran: pretty much we can now understand, like what the expected cost is gonna be. And so one thing I’m gonna do for the forecast is we’ll actually be able to also forecast the shipping costs.
92 00:11:41.060 ⇒ 00:11:44.949 Uttam Kumaran: you know, future shipping costs based on what we’re gonna hit on a skew
93 00:11:45.130 ⇒ 00:11:53.009 Uttam Kumaran: sales wise, which is great because we now we have, we have, like what we’re quoted, and that that’ll be additionally what we, I think when go to ups.
94 00:11:53.340 ⇒ 00:11:55.140 Uttam Kumaran: you know, and kind of mention, but
95 00:11:55.340 ⇒ 00:12:04.230 bencohen: I’m hoping that this, like we, haven’t done anything on like shipping delays or anything like that. So, but this is mainly on our side shipping. II wouldn’t
96 00:12:04.540 ⇒ 00:12:14.910 bencohen: everything down that side seemed pretty. Okay. I don’t have any real concern there. Okay. I’m not worried about like
97 00:12:15.180 ⇒ 00:12:19.100 bencohen: the quality of shipper, like, I think, all
98 00:12:19.690 ⇒ 00:12:23.370 bencohen: similar speed. They’re all handling packages
99 00:12:24.160 ⇒ 00:12:32.989 bencohen: about the same. It’s not like one has like a crazy incidence of damage return. Yeah, it’s really just a price thing.
100 00:12:33.180 ⇒ 00:12:37.590 Uttam Kumaran: Okay. the other thing is like.
101 00:12:37.600 ⇒ 00:12:50.309 Uttam Kumaran: yeah, I mean, this is kind of a just a basic like shipping calculator we could try to use again. I wanna try to get it in ship station automated. But pretty much here. You can just select like, if you want to use Fedex, what the way it is?
102 00:12:50.630 ⇒ 00:13:01.590 Uttam Kumaran: and then you can just put in the actual zone or if I leave the zone empty. You’ll kind of be able to see what the prices
103 00:13:01.970 ⇒ 00:13:06.180 bencohen: like. What’s interesting like like, there’s like weird things like, we know, that’s
104 00:13:07.180 ⇒ 00:13:08.810 bencohen: so. Okay, never mind.
105 00:13:09.580 ⇒ 00:13:11.910 bencohen: Well, why is it? I’m confused.
106 00:13:12.780 ⇒ 00:13:24.589 Uttam Kumaran: So this is linear, I guess, like this isn’t sorted by this isn’t sorted by zone right now. But like, for example, if I brought in, if I if I got rid of Fedex.
107 00:13:24.760 ⇒ 00:13:30.190 Uttam Kumaran: you’ll be able to see both, you’ll be able to see ups and Fedex in one.
108 00:13:31.840 ⇒ 00:13:48.439 Uttam Kumaran: and then but the this isn’t really a comparison. This is more just like, if you were to have one thing you wanted to calculate but I think I’m gonna I’m gonna put the. I’m gonna put some more information about the quote differences within this shipping dashboard so we can have that
109 00:13:51.160 ⇒ 00:13:57.410 Uttam Kumaran: the other thing we’ve been working on is a bit of
110 00:13:57.550 ⇒ 00:14:01.260 Uttam Kumaran: like goals and forecast. So that’s what I wanted to spend
111 00:14:01.470 ⇒ 00:14:14.520 Uttam Kumaran: a little bit of time talking today. What we did right now is we just assumed what what you mentioned, which is just like, let’s just roll with like 1.2 times sales. Right now, we actually have the ability to do
112 00:14:14.590 ⇒ 00:14:18.269 Uttam Kumaran: forecast on a sku and a month
113 00:14:18.730 ⇒ 00:14:19.960 Uttam Kumaran: level.
114 00:14:20.440 ⇒ 00:14:25.169 Uttam Kumaran: so I guess my question for you would be.
115 00:14:25.800 ⇒ 00:14:28.889 Uttam Kumaran: How should we tackle like gathering those?
116 00:14:28.910 ⇒ 00:14:33.179 Uttam Kumaran: And what what we’ll be able to do is not only show what
117 00:14:33.200 ⇒ 00:14:48.370 Uttam Kumaran: will not even be able to show, for example, what you’re seeing here is this forecast line is the forecast based on 2022. So we’re actually in some months we actually were overachieving, and some months in a good amount of months we underachieved in terms of just 1.2 times
118 00:14:48.550 ⇒ 00:14:58.499 Uttam Kumaran: 2022. This orange line going into next year is like 1.2 times green. Pretty much so it’s it’s just pretty much linear. However, I want us to. Now look at.
119 00:14:59.150 ⇒ 00:15:02.559 Uttam Kumaran: Do we want to set goals on a month and a ski level
120 00:15:03.320 ⇒ 00:15:11.039 bencohen: and then be able to understand both would be both would be good. Okay? Cause then you’re just breaking it down deeper.
121 00:15:11.100 ⇒ 00:15:20.559 Uttam Kumaran: Yeah. So I that’s what I wanna do. And then to even go further step, we’ll also be able to set goals for discount, for shipping.
122 00:15:20.890 ⇒ 00:15:27.039 Uttam Kumaran: for cost. Right? And that way every single parameter of profit will be able to.
123 00:15:27.050 ⇒ 00:15:41.950 Uttam Kumaran: We could potentially attach a goal towards and get a forecast of what we expect it to be. Because, for example, II don’t know what the discount next year, if we’re assuming the same percentage discount goals or assuming we’re gonna reduce that.
124 00:15:42.140 ⇒ 00:15:50.749 Uttam Kumaran: But like as a percentage of sales. But again, I think now we can kind of do that. So I don’t know what you think is the best way to kind of like.
125 00:15:51.090 ⇒ 00:15:57.090 Uttam Kumaran: I don’t know if it’s maybe me and you or me and you and Dan to kind of like, walk through what those could be.
126 00:15:57.950 ⇒ 00:16:02.410 Uttam Kumaran: we can just start with like skew level and month level goals.
127 00:16:02.870 ⇒ 00:16:06.570 bencohen: And then, yeah, I think
128 00:16:06.800 ⇒ 00:16:14.320 Uttam Kumaran: this is where I think there’s a it’ll. It’ll be great next year to be able to see on a on a weekly and a monthly basis whether we’re tracking
129 00:16:14.370 ⇒ 00:16:22.090 Uttam Kumaran: you know, and then and then ultimately, it’s even sorry. I’m just even going one step deeper
130 00:16:22.210 ⇒ 00:16:34.269 Uttam Kumaran: when we talk about achievement. We’ll also be able to look at contributors and detractors to that week. So, for example, if in a given week we’re like lagging, I’ll be able to share which skew is lagging
131 00:16:34.770 ⇒ 00:16:43.339 bencohen: or for hire, I’ll be able to show which skew is contributing more. Right? So that’s kind of the cascading
132 00:16:43.640 ⇒ 00:16:49.399 bencohen: it would. That’s helpful to see if something’s running hot or cold. it’s kind of it’s like
133 00:16:51.050 ⇒ 00:16:54.540 bencohen: based on feeling. We typically based on like 2 things like
134 00:16:54.990 ⇒ 00:17:13.539 bencohen: feeling. And also when I talk to the warehouse and say, You know I have my weekly inventory meeting, and I’ll even sometimes just have them go on facetime and just show me the warehouse, so I can see like sometimes. So between those 2, I always kind of know if something’s high or low, but it’d be nice to have like some
135 00:17:13.900 ⇒ 00:17:21.479 bencohen: kind of in the way you had, like the red and the dark red red to like over index under index.
136 00:17:21.540 ⇒ 00:17:27.119 bencohen: That would be helpful for me, because then I’m not like I don’t have to think all that much. It’s just kind of showing me.
137 00:17:27.240 ⇒ 00:17:39.669 Uttam Kumaran: No, exactly. So so really similar to like this overview on a skew level. You can not only see like this month this month last year the change, but you’ll also be able to see versus the forecast.
138 00:17:40.200 ⇒ 00:17:44.959 Uttam Kumaran: And then, of course, like, if we missed as as we achieve certain things.
139 00:17:45.340 ⇒ 00:17:56.469 Uttam Kumaran: if like, if we want to change our goals, the actual future forecast now is to change. So we’ll almost have like 3 scenarios. Right? We’ll have like actuals. We’ll have forecast, and then we’ll have like almost like a modified
140 00:17:56.630 ⇒ 00:18:05.200 Uttam Kumaran: forecast, or like a live forecast. And again, like I think on on a weekly and a monthly basis, you’ll really be able to see.
141 00:18:05.680 ⇒ 00:18:10.299 Uttam Kumaran: and I think it’s less about like interim. It’ll be more as like the weeks go by.
142 00:18:10.460 ⇒ 00:18:21.269 Uttam Kumaran: Okay, like for the last, like 2 months, we’re really lagging in the skew we need to make. There has to be some adjustment. If we wanna hit the goal right? So you’ll be able to see the risk a little bit
143 00:18:23.220 ⇒ 00:18:29.180 Uttam Kumaran: so I don’t know. What. What do you? What do you think it? Yeah, I think it. I think I liked. I liked.
144 00:18:29.590 ⇒ 00:18:33.680 bencohen: I think, both. I want to cease skew level goal setting
145 00:18:33.910 ⇒ 00:18:38.869 bencohen: and then the multiplier of a previous period setting.
146 00:18:38.990 ⇒ 00:18:42.130 bencohen: Okay for me, most helpful is seeing
147 00:18:42.430 ⇒ 00:18:57.380 bencohen: day to day if something is hot or cold against the goal. So you know, II didn’t know how important it was to do on a daily like if we assess that we think we can sell 4 million dollars of one specific skew in a year.
148 00:18:57.400 ⇒ 00:18:59.729 bencohen: Yeah, okay, well, to achieve
149 00:18:59.970 ⇒ 00:19:06.960 bencohen: 4 mail of the of this skew over 12 months. We need to do this. And then, you know, obviously, there’s certain.
150 00:19:07.290 ⇒ 00:19:23.720 Uttam Kumaran: And then, you know, it’s a little bit micro. But then you say, okay, well, that that means sells 6 a day. No, you you can. Yeah, that. That’s exactly what like again for me, we can go all the way to that level. It’s really what’s helpful like I. The thing about daily, is it? I would say
151 00:19:23.780 ⇒ 00:19:45.459 bencohen: it would be, do you have levers that you could pull on a daily basis to affect that? Or is it just like, is it just like stress cause? That’s that’s what I’ve seen in the past with companies is like, if they do daily, it’ll just like have a cause. It’ll be red or green. It’ll be red or green one way or another. I think it’s a morale killer. So I think that that’s, I think, make it that granular.
152 00:19:45.530 ⇒ 00:19:46.680 bencohen: Okay?
153 00:19:48.160 ⇒ 00:20:03.700 bencohen: weekly. Yeah, I don’t know. It’s we could do it right. And it it is a number it’s like, so, well, yeah, I mean, listen. If you if you do the monthly one, it’s very. The math is very simple, it just divided by 30, and you know roughly where you are on the day. But let’s just do it
154 00:20:03.840 ⇒ 00:20:13.279 Uttam Kumaran: for the month for now, and you’ll see it fill up over the month as the month goes by. You’ll kind of see, like you’ll literally see a bar fill up, and then you can tell.
155 00:20:13.320 ⇒ 00:20:21.799 Uttam Kumaran: Are we going to hit the month or not? Because what I’ll do is I’ll I’ll look at like what we’ve been hitting the rate at which we’re going. And you’ll say, like, it’ll be like a yes or no.
156 00:20:22.100 ⇒ 00:20:29.800 Uttam Kumaran: you think is, does the math show we’re gonna hit? And then, of course, like, I think, even back to kind of what is some of the stuff we talked when we originally
157 00:20:29.930 ⇒ 00:20:42.230 Uttam Kumaran: kinda got together is like, I wanna start doing some more like advanced statistical things. And there’s some new linear regression and things like that that we can help do more accurate forecast versus just
158 00:20:42.360 ⇒ 00:20:53.090 Uttam Kumaran: just a basic rate. So that’ll be the evolution of that. But yeah, so maybe we can have a conversation. And just, I literally just need a spreadsheet with like skew months
159 00:20:53.910 ⇒ 00:20:58.589 Uttam Kumaran: goal. Or if you, if you think it’s like less specific than that.
160 00:20:58.710 ⇒ 00:21:02.649 bencohen: I don’t know like, what what do you think we should do, I think. No, I think just
161 00:21:03.220 ⇒ 00:21:26.219 bencohen: II don’t. Wanna. I wanna spend more time on on the shipping and and redo, and like improving marketing efficiency in terms of spend. So I don’t want to get too carried away here. But I think that if you, as a simple rule, you just make like the multiplier. So there’s like a settings page where I can say our goal is to achieve a a 26% increase year over year. And then that’s enough
162 00:21:26.350 ⇒ 00:21:27.860 bencohen: for you to
163 00:21:27.960 ⇒ 00:21:33.809 bencohen: to figure out. You know what what the, what the goal would be, and then it could be variables. We can change it if we see
164 00:21:34.180 ⇒ 00:21:39.889 bencohen: whatever inflation goes up more, you know, whatever the case may be, we can start like I can tune it
165 00:21:40.000 ⇒ 00:21:42.699 Uttam Kumaran: okay? And then, so so what I’ll do is
166 00:21:43.220 ⇒ 00:21:53.380 Uttam Kumaran: in A in A, I’ll take the high level goal, and then we’ll look at how each skew or product category has contributed to that. And we’ll evenly distribute
167 00:21:53.650 ⇒ 00:22:01.140 Uttam Kumaran: like that goal instead of just saying, like 1.2 across the board and saying, every skew has to grow 20%, it’ll grow
168 00:22:01.300 ⇒ 00:22:05.699 bencohen: linear to its contribution if that makes sense. Yeah, yeah, I think that’s fine.
169 00:22:05.900 ⇒ 00:22:12.910 Uttam Kumaran: Okay, so then let’s try that. And I’ll I’ll have a spreadsheet where you can literally just put that in, and then it’ll flow in
170 00:22:13.480 ⇒ 00:22:14.830 bencohen: perfect perfect.
171 00:22:15.010 ⇒ 00:22:29.590 Uttam Kumaran: And then yeah, on the sha on the shipping side. So yeah, today I want to try and mess around with some of the ship station things, and then I don’t. So tell me what you think we should do for the ups conversation. My thought was.
172 00:22:29.590 ⇒ 00:22:50.940 bencohen: I’m gonna call Chuck and kinda say, like, Tell me a couple of skews or a couple of zones, or a couple of months that you think I should like focus on a little bit of a data sheet around that. And what I what I find it might take me like an hour. Yeah, just we. The goal is to move the needle and get them to offer us some crazy deal to like exclusive with them, so
173 00:22:50.940 ⇒ 00:22:55.020 bencohen: data will be the most helpful thing.
174 00:22:56.150 ⇒ 00:23:16.130 Uttam Kumaran: I don’t know. I try to stay away from all that stuff. It’s too brandy. No, no, no, I mean II just for me. It’s more like what what I’m thinking is like. I want to show both what we did this year. And then the reason why the goals matter is cause I want to show what we’re gonna do next year. And then I wanted I want to have both of those be like.
175 00:23:16.890 ⇒ 00:23:21.050 Uttam Kumaran: Show me like, show me what we can do about that discount factor or
176 00:23:21.150 ⇒ 00:23:23.769 Uttam Kumaran: giving a better rat sheet.
177 00:23:24.060 ⇒ 00:23:29.450 Uttam Kumaran: you know, and I think that’ll be tremend. I think she’ll. I think she’ll do that for us. So
178 00:23:31.150 ⇒ 00:23:39.080 bencohen: perfect. Yeah, just give text or a call, and and he knows exactly like what what moves
179 00:23:39.250 ⇒ 00:23:40.550 bencohen: these people are.
180 00:23:42.270 ⇒ 00:23:53.019 Uttam Kumaran: And then I guess, how how do we feel about like the I know we didn’t. We’re just now talking about doing. Did you guys end up doing the orders for next year? Or, what’s the
181 00:23:54.110 ⇒ 00:23:59.419 bencohen: yeah? I mean, we sent our what we we send stuff.
182 00:23:59.680 ⇒ 00:24:24.369 bencohen: It’s not. It’s very informal. So the fact is, like, Okay, great thanks. Alright. I get it. Or we’re gonna go away for 2 weeks because it’s Chinese New Year. And then we’re also like, well, inventory. There’s also stuff produced already waiting in, palatized in China, waiting to come. So I don’t. I don’t see it as like a huge emergency at this state. And I think that’s probably why it’s always so informal. But
183 00:24:24.490 ⇒ 00:24:26.190 bencohen: yeah.
184 00:24:26.800 ⇒ 00:24:34.930 Uttam Kumaran: Yeah. And again, I think a lot of that cascades from the goal, because we’ll be able to see what the quantity forecast is.
185 00:24:35.240 ⇒ 00:24:36.660 bencohen: Yeah. And then.
186 00:24:36.780 ⇒ 00:24:41.310 Uttam Kumaran: right now, we’ll be able to show. For example, like I have.
187 00:24:41.620 ⇒ 00:24:46.450 Uttam Kumaran: We have. We have an inventory like when the supply is coming in.
188 00:24:46.770 ⇒ 00:24:52.790 Uttam Kumaran: and then the rate at which this draws down. So again, I think, like we’ll pretty easily be able to see like
189 00:24:52.960 ⇒ 00:25:07.349 Uttam Kumaran: these things certain skews fly, and it takes about X days to deplete the shipment. And then here’s like the amount we need to order. And maybe I’ll just put that in front of you. I think we’re we’re pretty close to that, anyway. So yeah.
190 00:25:07.550 ⇒ 00:25:24.609 bencohen: yeah, no, it’s looking great. It’s looking great. Alright. So yeah, get the chuck. And let’s see what kind of action we can get out of the the carriers at the minimum. II do like that. We can have ship station pick them more efficient, I think. Already. Off of that. You have a couple of points of
191 00:25:24.860 ⇒ 00:25:26.260 bencohen: saving.
192 00:25:26.370 ⇒ 00:25:30.350 bencohen: But the real goal is like that. Ups says, you know what
193 00:25:30.550 ⇒ 00:25:32.429 bencohen: you ship all these parcels.
194 00:25:32.720 ⇒ 00:25:36.530 bencohen: I’m just gonna cut 10% off of everything.
195 00:25:36.900 ⇒ 00:25:55.440 bencohen: and but you have to be exclusive like that would be. We did that with Fedex years ago, when we were making the jump from like 3 million a year to like. And you know, we said we we you know we’re we’re, you know, a year, a year and change old like, you know, we were paying terrible rates. And anyway, they did it. So
196 00:25:55.560 ⇒ 00:26:03.339 Uttam Kumaran: okay, I don’t see why. I mean, yeah. She seems pretty receptive, and I think we just blaster with a ton of data and see, like.
197 00:26:04.490 ⇒ 00:26:09.140 Uttam Kumaran: yeah. And now that I kind of know what the discount, how they do the calculations.
198 00:26:09.390 ⇒ 00:26:21.109 Uttam Kumaran: We’ll see what she says. Yeah, I think it should be. I think it should go in that direction, because it cause additionally, what I’ll be able to do is like, if she gives us a quote, I’ll plug it in and see like, what that does
199 00:26:21.230 ⇒ 00:26:24.430 bencohen: for the yeah. Oh, yes, and shipping right?
200 00:26:24.560 ⇒ 00:26:31.450 bencohen: Live data there. So you’ll know exactly how much of a of a break we’re gonna get. Yeah, yeah. So
201 00:26:32.200 ⇒ 00:26:54.630 bencohen: and I don’t even think they have that on their side, which is kind of crazy, cause II was like asking your budget questions like, I don’t really know if the poll that for you I’m like, but I think that that works in Fe in our favor, because then she might totally give up. She might just be like she probably has, like 14 point percent of room that she knows she’s allowed to operate within. Obviously for her, it’s best to keep us at the higher.
202 00:26:54.770 ⇒ 00:27:01.989 bencohen: But she, if if we push her at all, I think she can just be okay. Well, I’ll make one on these these assholes. And and yeah, yeah.
203 00:27:02.380 ⇒ 00:27:05.060 I think that’s how it works.
204 00:27:05.270 ⇒ 00:27:20.890 bencohen: Okay, yeah, I don’t even know how they get comped or anything. That’s usually what I try to find out. Yeah, I think it’s based on revenue. So really small part of her business. So I think, showing her that Fedex is getting
205 00:27:23.340 ⇒ 00:27:27.299 bencohen: 90 times the business. Yeah, you know.
206 00:27:27.450 ⇒ 00:27:28.380 Uttam Kumaran: Yeah.
207 00:27:28.750 ⇒ 00:27:31.870 bencohen: that’s how I think that’s how I think it can go.
208 00:27:32.330 ⇒ 00:27:33.409 Uttam Kumaran: Okay, cool.
209 00:27:34.680 ⇒ 00:27:39.879 Uttam Kumaran: Okay. So let me follow up on the goals thing and then kind of
210 00:27:40.070 ⇒ 00:27:43.050 Uttam Kumaran: try some stuff with Chuck and Strip station. And then.
211 00:27:43.130 ⇒ 00:28:00.450 bencohen: yeah, we’ll try to get back to Ups. I think we can get back to this month hopefully by the end of the month. I know it’s Thanksgiving. But no, no, we gotta be soon. I think we can get a deal done with them like this week like they’re they’re ready. I don’t. I don’t wanna drag the feet, even if we don’t have complete. I wanna still push them.
212 00:28:00.560 ⇒ 00:28:02.100 Uttam Kumaran: Okay, okay, okay.
213 00:28:02.580 ⇒ 00:28:06.489 bencohen: yeah. Just get to chuckle, chuckle. Now, if that person’s available to talk.
214 00:28:06.670 ⇒ 00:28:17.110 bencohen: you give them a couple of things and show them how you’re thinking we cannot. I don’t want you to burn a lot of hours, because I think they’re going to do it, no matter what.
215 00:28:17.540 ⇒ 00:28:22.979 bencohen: Alright, I’ll talk to you. Let me know it goes with chuck. Okay, cool. Thanks, man. Good job.
216 00:28:23.430 ⇒ 00:28:24.470 Uttam Kumaran: Appreciate it.