Meeting Title: Brainforge Inventory Data Sync Date: 2025-07-09 Meeting participants: Emily Giant, Felipefaria, Demilade Agboola
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1 00:00:50.880 ⇒ 00:00:52.030 Emily Giant: Good morning!
2 00:00:58.740 ⇒ 00:01:00.249 Demilade Agboola: Hello! How are you?
3 00:01:01.670 ⇒ 00:01:09.236 Emily Giant: Good. I slept last night, so that’s always a step in the right direction. So I feel like a human again.
4 00:01:09.990 ⇒ 00:01:12.889 Demilade Agboola: That’s a very important part of being human.
5 00:01:13.190 ⇒ 00:01:23.340 Emily Giant: Yes, it is. It is. It turns out I’m just a terrible one when I don’t. 1 second we have a Npr. Playing in the background, and it’s so loud that I like can barely
6 00:01:33.410 ⇒ 00:01:44.310 Emily Giant: sorry. I listen to the news like when I’m having my coffee. And the news was just still going so loudly. I think Felipe should be joining, but
7 00:01:45.000 ⇒ 00:01:55.669 Emily Giant: we don’t have to wait on him. We can. We can kick it off. I have the dashboards ready to review with him, too, so that we can like knock that ticket out.
8 00:01:56.060 ⇒ 00:01:57.060 Emily Giant: But.
9 00:01:57.350 ⇒ 00:02:00.440 Demilade Agboola: How are you doing? Have you been alrighty.
10 00:02:00.590 ⇒ 00:02:01.679 Emily Giant: What’d you say?
11 00:02:01.960 ⇒ 00:02:03.559 Demilade Agboola: Oh, you built all the dashboards already.
12 00:02:03.850 ⇒ 00:02:07.910 Emily Giant: I I know. No, they’re built the ones that will be affected so.
13 00:02:07.910 ⇒ 00:02:08.340 Demilade Agboola: Oh, okay.
14 00:02:09.680 ⇒ 00:02:25.339 Emily Giant: No, I worked on just like clearing out some of my my dumb tickets yesterday, so that, like they weren’t littering the the progress. And then I’m gonna work on like once we’ve
15 00:02:25.480 ⇒ 00:02:32.619 Emily Giant: like solidified some naming stuff and gone over it with Felipe. Today I was gonna build out the looker stuff with inventory.
16 00:02:32.780 ⇒ 00:02:33.100 Demilade Agboola: Same time.
17 00:02:33.390 ⇒ 00:02:38.429 Emily Giant: Dashboards real quick. But amber wanted a list of
18 00:02:38.690 ⇒ 00:02:41.649 Emily Giant: dashboards that will be like most impacted.
19 00:02:42.000 ⇒ 00:02:50.120 Emily Giant: So I listed those out. I just wanna check in with him to see if I’m missing any of them. Hold on!
20 00:02:50.260 ⇒ 00:02:52.060 Emily Giant: I just got a DM.
21 00:02:52.300 ⇒ 00:02:57.305 Emily Giant: He said, hopping in the meeting in 2 min perfect.
22 00:02:58.310 ⇒ 00:02:59.800 Emily Giant: See you soon.
23 00:03:02.820 ⇒ 00:03:05.907 Emily Giant: He should be here in 2 min. Okay
24 00:03:08.769 ⇒ 00:03:18.179 Emily Giant: any any progress on your end? We didn’t really like End yesterday with any action items we just had to like hop. So
25 00:03:18.380 ⇒ 00:03:19.979 Emily Giant: I feel like we’re still
26 00:03:20.370 ⇒ 00:03:28.159 Emily Giant: in the process of doing what we were doing yesterday, which is like sorting out afs and making sure the canceled sales
27 00:03:28.520 ⇒ 00:03:33.499 Emily Giant: are correctly flagged in inventory. But I know that, like
28 00:03:33.980 ⇒ 00:03:37.280 Emily Giant: hard goods and non-florals, are like
29 00:03:37.580 ⇒ 00:03:41.710 Emily Giant: the next step after. So is there anything you wanted to like
30 00:03:42.900 ⇒ 00:03:46.090 Emily Giant: definitely hit with Felipe 1st when he hops on.
31 00:03:47.382 ⇒ 00:03:50.800 Demilade Agboola: So for me with Felipe, I just want us to be able to
32 00:03:50.920 ⇒ 00:04:00.699 Demilade Agboola: be in sync on the numbers that we have, and just be sure that there isn’t any number, or there are any numbers that he wants to see that we don’t currently have
33 00:04:01.927 ⇒ 00:04:06.479 Demilade Agboola: so like, I can mentally like, check that part as done.
34 00:04:07.257 ⇒ 00:04:14.919 Demilade Agboola: I mean, obviously, I understand that like random use cases who come up that we’ll need to like tweak. But I mean, just like the entire framework of that is done.
35 00:04:15.464 ⇒ 00:04:21.060 Demilade Agboola: So I can like mentally moving to like target and just kind of like owning on that as well.
36 00:04:21.260 ⇒ 00:04:21.760 Demilade Agboola: Oh.
37 00:04:21.760 ⇒ 00:04:23.289 Emily Giant: All right. That’s good.
38 00:04:23.500 ⇒ 00:04:24.450 Emily Giant: Yeah.
39 00:04:26.350 ⇒ 00:04:31.789 Emily Giant: I’m trying to think I know that there are questions about like
40 00:04:33.460 ⇒ 00:04:38.836 Emily Giant: on my end, anyway. So when we were doing Qa the other day.
41 00:04:41.990 ⇒ 00:04:48.637 Emily Giant: You know what? Maybe I won’t even worry about it as long as the numbers make sense to their team.
42 00:04:49.250 ⇒ 00:04:56.660 Emily Giant: the shrinkage and and, like mismatch inventory count numbers.
43 00:04:58.710 ⇒ 00:05:06.080 Emily Giant: They always like balance out the lot back to where
44 00:05:06.630 ⇒ 00:05:19.170 Emily Giant: only the amount that was received is sold, but those numbers are like often duplicates of sales
45 00:05:19.630 ⇒ 00:05:32.259 Emily Giant: that were sent out on the lot, like, for example, there was the one that you used for Qa. Those 3 lots and one had negative 12 in. Shrinkage and negative in like inventory mismatch.
46 00:05:32.800 ⇒ 00:05:33.450 Demilade Agboola: Yeah.
47 00:05:33.640 ⇒ 00:05:38.199 Emily Giant: And that’s because, like 8 of them, 8 units
48 00:05:38.460 ⇒ 00:05:41.329 Emily Giant: were sent out the door without a commitment.
49 00:05:41.480 ⇒ 00:05:42.485 Emily Giant: And
50 00:05:44.650 ⇒ 00:05:58.260 Emily Giant: I suppose we’re reflecting what’s in the system, and that’s what they want. So it’s fine, and we shouldn’t worry about it. But those weren’t shrink. Those were sales, and I always like revert back to this thought of like.
51 00:05:59.220 ⇒ 00:06:05.140 Emily Giant: should the data be correcting for what’s incorrect in the system like.
52 00:06:05.250 ⇒ 00:06:10.881 Emily Giant: or do we just want to reflect what’s in the system. I know we don’t need to be superheroes. But like.
53 00:06:12.780 ⇒ 00:06:13.980 Emily Giant: Oh, yeah, right?
54 00:06:14.170 ⇒ 00:06:16.860 Emily Giant: And we did make money with those units.
55 00:06:19.450 ⇒ 00:06:20.130 Demilade Agboola: Because.
56 00:06:20.130 ⇒ 00:06:25.770 Emily Giant: It didn’t work. How it’s supposed to. It’s like categorized as shrink.
57 00:06:27.110 ⇒ 00:06:28.869 Emily Giant: But we didn’t do that.
58 00:06:29.470 ⇒ 00:06:34.599 Demilade Agboola: No, no, yeah. I mean, it’s definitely not what we did. And it’s categorized a shrink in the systems.
59 00:06:35.040 ⇒ 00:06:43.109 Demilade Agboola: ultimately, I think one of the things I’ve learned from one of my former managers when I used to work, was, we reflect what is in system.
60 00:06:44.190 ⇒ 00:06:52.370 Demilade Agboola: however, unless the person who is going to use the data wants the data to mean something else.
61 00:06:53.030 ⇒ 00:07:01.119 Demilade Agboola: That’s when like, that’s when we hop in and try and do that. So we reflect like, you know, this comes in a shrinkage, and this is shrinkage.
62 00:07:01.280 ⇒ 00:07:10.479 Demilade Agboola: And then they might say, Well, actually, as I look at it from a revenue perspective. The fact that we still sold something does not, does not like it doesn’t matter shrinkage to me.
63 00:07:11.134 ⇒ 00:07:17.599 Demilade Agboola: And so that could potentially be like how that is adopted for that particular use case.
64 00:07:17.790 ⇒ 00:07:21.520 Demilade Agboola: But I’m sure people there’s certain people will want to know, like
65 00:07:21.900 ⇒ 00:07:26.869 Demilade Agboola: those numbers were non-committed, and we want to know? Like why that happened.
66 00:07:27.584 ⇒ 00:07:45.879 Demilade Agboola: And just be on top of it so that they’re like Kpi. Maybe shrinkage should be for any period should be under what 10 or 20, or whatever number they might give them across all lots assigned to me. It’s easy for them to have a kpi where they can kind of see what’s going on every single time. So.
67 00:07:49.220 ⇒ 00:07:54.820 Emily Giant: So that’s more of like making sure that the there’s a field called like committed
68 00:07:55.749 ⇒ 00:07:59.070 Emily Giant: but right now it’s just numeric if I can
69 00:08:00.030 ⇒ 00:08:03.140 Emily Giant: make that more like ux friendly, and
70 00:08:03.760 ⇒ 00:08:07.389 Emily Giant: that way you could always pull in
71 00:08:09.530 ⇒ 00:08:27.619 Demilade Agboola: Like the commitment status of those orders that went out the door that didn’t claim and reconcile those. But yeah, that that makes complete sense. So in that in that light I don’t think we need to do anything to change the representation of how those numbers appear now. It’s just explaining to the stakeholders, like.
72 00:08:27.970 ⇒ 00:08:34.709 Emily Giant: If you’re using this to audit the system for like commitments, here’s your dashboard.
73 00:08:34.820 ⇒ 00:08:45.310 Emily Giant: But if you’re just looking for like sales, here you go, but you already have that to the extent
74 00:08:45.730 ⇒ 00:08:54.520 Emily Giant: that it all makes sense to me like the way you have it. Now the lots balance, and I think that that biggest challenge is just. There shouldn’t be more
75 00:08:54.890 ⇒ 00:08:59.409 Emily Giant: units adjusted than what existed.
76 00:09:00.530 ⇒ 00:09:03.050 Demilade Agboola: Yeah, yeah, I think,
77 00:09:07.450 ⇒ 00:09:12.529 Demilade Agboola: yeah, I think, ultimately, yeah, I think it’s just we do have the raw numbers about representation
78 00:09:14.040 ⇒ 00:09:18.249 Demilade Agboola: and just been able to put those numbers together.
79 00:09:18.590 ⇒ 00:09:19.280 Demilade Agboola: So.
80 00:09:20.770 ⇒ 00:09:21.460 Emily Giant: Yeah.
81 00:09:22.020 ⇒ 00:09:23.530 Emily Giant: Well, do you want me to?
82 00:09:23.690 ⇒ 00:09:29.699 Emily Giant: Sorry. Go ahead. Sleep right now. He’s just giving me like a play by play of his computer, not working
83 00:09:30.120 ⇒ 00:09:31.540 Emily Giant: just like, kind of.
84 00:09:32.750 ⇒ 00:09:40.120 Demilade Agboola: I I was gonna ask about the hard goods. Where? Where’s that data found like? Is it still in the same?
85 00:09:40.529 ⇒ 00:09:45.479 Demilade Agboola: But it’s just how is it identified? And how do we want to represent it?
86 00:09:46.170 ⇒ 00:09:52.990 Emily Giant: So it’s in the same table, and it’s its own row. So every item that still holds true.
87 00:09:53.280 ⇒ 00:09:59.139 Emily Giant: The thing is, there’s no lot, so you can’t unless it has an expiration date.
88 00:09:59.960 ⇒ 00:10:04.797 Emily Giant: I’m going to pull this up. It’s probably easier to look at than to explain.
89 00:10:07.150 ⇒ 00:10:12.630 Emily Giant: so some hard goods are allotted, meaning they have an expiration date. So that’s going to be like chocolate
90 00:10:13.127 ⇒ 00:10:25.149 Emily Giant: and then the majority don’t have lots because they don’t expire. So they’re just like a number. Felipe honestly, might be able to explain this better than me, because he’s very
91 00:10:26.040 ⇒ 00:10:52.029 Emily Giant: in the world of hard goods. But it’s in the same table as it’s the exact same process with just a different item class which we have renamed in staging model as product type. Id, so a product type. Id for florals and plants are one and 2 hard goods are like 3, 4, 5, something like that. But it’s in the item
92 00:10:52.300 ⇒ 00:10:56.400 Emily Giant: model that you’ll see that so they’re their own row.
93 00:10:56.660 ⇒ 00:11:03.189 Emily Giant: So it’s that same like, item, id plus sub order id is the uniqueness
94 00:11:03.675 ⇒ 00:11:09.889 Emily Giant: but they will not be in the same row as the main like parent product of the order
95 00:11:12.620 ⇒ 00:11:13.540 Emily Giant: here.
96 00:11:17.750 ⇒ 00:11:22.010 Emily Giant: Item, it’s called inventory item.
97 00:11:26.970 ⇒ 00:11:29.449 Emily Giant: So, looking at the lineage, it’s
98 00:11:30.240 ⇒ 00:11:34.889 Emily Giant: this. This came straight from like netsuite. Their query tool.
99 00:11:35.610 ⇒ 00:11:36.310 Demilade Agboola: Okay.
100 00:11:37.102 ⇒ 00:11:43.380 Emily Giant: So the lotted, the lot of products are the plant floral.
101 00:11:43.630 ⇒ 00:11:47.299 Emily Giant: We don’t do dried anymore, but I still include it.
102 00:11:47.410 ⇒ 00:11:52.699 Emily Giant: I don’t think those have an expiration date, though. So tbd on this one.
103 00:11:56.480 ⇒ 00:12:00.050 Emily Giant: So downstream.
104 00:12:03.740 ⇒ 00:12:08.810 Emily Giant: Nope, not that one, not that one here.
105 00:12:09.600 ⇒ 00:12:31.649 Emily Giant: So I have a temp table, and it’s the non lotted products. The biggest difference is you can’t tie it to the inventory assignment table because it won’t. Oh, Hi, Felipe, it won’t exist. You can only use the transaction and transaction line tables for hard goods, but it will like if you pull up an order.
106 00:12:33.560 ⇒ 00:12:39.290 Demilade Agboola: So my question is, what do we want to represent this
107 00:12:39.610 ⇒ 00:12:46.089 Demilade Agboola: in the inventory like, since there are not a lot ids associated with it. Are we just going to go
108 00:12:46.780 ⇒ 00:12:51.450 Demilade Agboola: like, how do we want to represent? It’s in a table in a tableau phone, basically.
109 00:12:51.450 ⇒ 00:12:53.939 Emily Giant: That is a question for Felipe.
110 00:12:54.740 ⇒ 00:13:00.580 Emily Giant: So I’m glad you’re here. So we’re talking about hardware. You want them to look in that lot balance table
111 00:13:00.860 ⇒ 00:13:02.259 Emily Giant: when they don’t have lots.
112 00:13:04.190 ⇒ 00:13:10.585 felipefaria: So sorry cause I joined. And you guys were in the middle of this process. So and and for hard goods
113 00:13:11.000 ⇒ 00:13:14.339 felipefaria: obviously, like most of the targets. We don’t have lots right.
114 00:13:15.970 ⇒ 00:13:24.260 felipefaria: I I saw you saying, Emily, that we would have to put that information strictly from transactions right? We would just
115 00:13:24.660 ⇒ 00:13:32.230 felipefaria: like if for our goods, the it’s basically the same thing as the florals. The main information that we would wanna have is
116 00:13:32.710 ⇒ 00:13:34.951 felipefaria: how many units we sold
117 00:13:35.730 ⇒ 00:13:50.285 felipefaria: like in any given week. I see that you you shared the the new report that you created Emily. It seems to be working. Like. As I mentioned, there is a small variation in the volume. Seems like is
118 00:13:50.980 ⇒ 00:13:55.847 felipefaria: Looker is a little bit over represented on that one. But
119 00:13:56.290 ⇒ 00:13:57.283 Emily Giant: Cancel orders.
120 00:13:58.300 ⇒ 00:14:13.849 felipefaria: Okay, yeah, and in terms of the dates I saw that there was no option for fulfillment date in that table. For some reason. So I’m just going by delivery date, which is fine by now. But ideally, we would have fulfillment date as well.
121 00:14:14.466 ⇒ 00:14:27.673 felipefaria: And then just having information in terms of like, what do we still have available for sale? Right? We can like, I guess we can have. We can have the on hand as well. And
122 00:14:30.292 ⇒ 00:14:42.917 Emily Giant: We’re still working through that like I did some improvements done a lot of the Afs. But so the hard, good stuff is getting better. The misrepresentation is, definitely like what
123 00:14:43.750 ⇒ 00:15:04.490 Emily Giant: Demo A is still trying to fix and make perfect. But this is like the intermediate table. So this isn’t what you’re going to see in Looker. You could create this, but we work from like a very so you know how we always do Qa, with lots, and it’s like, well, we don’t look at stuff on the lot level all the time from your position, but that’s the best way for Demo Lotte and I to like
124 00:15:04.620 ⇒ 00:15:11.269 Emily Giant: control the numbers and make sure that like in that unit, that what’s represented is perfect, and
125 00:15:11.270 ⇒ 00:15:11.680 Emily Giant: and you can
126 00:15:11.680 ⇒ 00:15:23.040 Emily Giant: roll it up into bigger numbers like by Fc. But this is the best way for us to control like absolute accuracy, and because hard goods don’t have lots, our question is like.
127 00:15:23.190 ⇒ 00:15:28.009 Emily Giant: how are we supposed to roll these up like? What is the box that we
128 00:15:28.380 ⇒ 00:15:36.150 Emily Giant: put these in when inventory number Id is what we use for every other thing. But hard goods don’t necessarily.
129 00:15:37.420 ⇒ 00:15:48.757 felipefaria: I mean, and can can we just leave it blank like, because we we know that there’s no lot right like there’s only shipments.
130 00:15:49.440 ⇒ 00:16:01.110 felipefaria: so we wouldn’t necessarily be looking for that information. Right? As I said, it’s it’s mainly just looking at. How many do we sell, and how many in total, we have afs and on hand.
131 00:16:01.860 ⇒ 00:16:06.349 felipefaria: so I wouldn’t necessarily be looking for an inventory number. Id.
132 00:16:07.190 ⇒ 00:16:08.659 felipefaria: Because I know that
133 00:16:09.670 ⇒ 00:16:15.960 felipefaria: they don’t have one except for some lauded hard goods. Right? And ideally we we would have.
134 00:16:16.120 ⇒ 00:16:24.380 felipefaria: And the thing about the the lauded Hardgoods is ideally we would have a table that shows both lotted and allotted because
135 00:16:24.890 ⇒ 00:16:37.040 felipefaria: it’s just a little bit more streamlined to pull the information, because, like, you know, when I’m looking at hard, good sales ideally, I would just look at one spreadsheet that has all the hard goods, regardless of whether it’s slotted or not. It?
136 00:16:38.800 ⇒ 00:16:44.709 felipefaria: so so yeah, Mike, I don’t know if that answers the question, but I’m not really too concerned about.
137 00:16:46.606 ⇒ 00:16:48.120 Demilade Agboola: That column.
138 00:16:48.670 ⇒ 00:16:55.309 Demilade Agboola: I think my takeaway from this is and correct me if I’m wrong, is that we should aggregate it on an item
139 00:16:55.420 ⇒ 00:16:56.880 Demilade Agboola: id basis.
140 00:16:57.300 ⇒ 00:17:00.579 Demilade Agboola: But for each item this is this quantity sold?
141 00:17:01.010 ⇒ 00:17:04.010 Demilade Agboola: Oh, my, okay, alright, that’s good.
142 00:17:04.560 ⇒ 00:17:08.060 Emily Giant: And that the way to delineate whether or not
143 00:17:09.380 ⇒ 00:17:18.070 Emily Giant: oh, no, I guess it will just have a lot id if you use inventory assignment table to pull the final sales. Then
144 00:17:18.359 ⇒ 00:17:25.179 Emily Giant: it’s got a lot number, if not, but there is a demo lotto. You’ll see when you start digging in here. That there’s a
145 00:17:26.339 ⇒ 00:17:31.500 Emily Giant: field called is lauded is non lauded. And like, that’s how I
146 00:17:32.070 ⇒ 00:17:44.830 Emily Giant: in the original table. That’s how I built it out. I was like, Okay, not a lot at once. You go to this table, a lot of ones. You go to this, but I do roll them all up into the same mart. And that’s not great, probably.
147 00:17:45.900 ⇒ 00:17:53.190 Emily Giant: unless you, because, like the testing we do is on like the uniqueness of inventory numbers. So that’s gonna like screw up
148 00:17:53.510 ⇒ 00:17:56.760 Emily Giant: are like Dbt tests.
149 00:17:57.650 ⇒ 00:18:03.029 Emily Giant: Not a big deal. But we still want to make sure that, like we’re not getting dupes.
150 00:18:03.910 ⇒ 00:18:10.300 Demilade Agboola: So what we could do is we could do it could be a combination of the inventory number Id and the item id
151 00:18:10.850 ⇒ 00:18:18.619 Demilade Agboola: line. So where it’s null, it will just kind of be like a test that the item Id is unique. If that makes any sense.
152 00:18:18.980 ⇒ 00:18:19.650 Emily Giant: It does.
153 00:18:19.930 ⇒ 00:18:28.709 Demilade Agboola: Yeah, so, and that’s fine. I think that works for us going forward. I just wanted to be clear on how we wanted to represent it to use.
154 00:18:28.840 ⇒ 00:18:33.179 Demilade Agboola: And also another question is with the item, Id.
155 00:18:34.210 ⇒ 00:18:36.780 Demilade Agboola: are we able to tie back to
156 00:18:37.260 ⇒ 00:18:45.870 Demilade Agboola: the Fcs. Because I don’t know if you might want to roll it up, based off like locations and stuff. I don’t know if that data is available. That’s what I’m asking.
157 00:18:46.240 ⇒ 00:18:47.120 Emily Giant: You can.
158 00:18:47.380 ⇒ 00:18:48.050 Demilade Agboola: You cannot.
159 00:18:48.050 ⇒ 00:18:58.930 Emily Giant: Transaction line, so like the draft of what you’re describing is in this int suborders, not loaded.
160 00:18:59.320 ⇒ 00:19:04.169 Demilade Agboola: Alright! Alright! I’ll I’ll take a look at it and try and build out the inventory table for that.
161 00:19:04.720 ⇒ 00:19:06.350 Emily Giant: Okay, perfect.
162 00:19:08.710 ⇒ 00:19:09.520 Emily Giant: Yeah.
163 00:19:09.630 ⇒ 00:19:15.230 Emily Giant: So this one was like, roughly working pretty well outside of
164 00:19:15.360 ⇒ 00:19:20.685 Emily Giant: for hard goods. For me. It was qaing pretty well, outside of like canceled items.
165 00:19:21.830 ⇒ 00:19:25.846 Emily Giant: enforced upgrades. But we’re correcting for that.
166 00:19:26.870 ⇒ 00:19:35.810 Emily Giant: like you’ve corrected for that in your like in Ag model. So when this one’s updated, it will correct. The thing that
167 00:19:36.160 ⇒ 00:19:39.809 Emily Giant: I’m still unclear on. I don’t know if we want to go there today, but I’m just throwing it out. There is
168 00:19:40.540 ⇒ 00:19:48.400 Emily Giant: non-florals, and how the team wants those to be viewed because it has changed over time.
169 00:19:50.010 ⇒ 00:20:10.123 Emily Giant: It used to be like the pot and the plant were inventoried separately, as like a hard good and a live product. And then over time, because they’re never sold without one another like the bouquets. They became like a permanently kitted one skew thing. This like goes like this over time.
170 00:20:11.060 ⇒ 00:20:12.770 Emily Giant: And I don’t know if the team
171 00:20:13.620 ⇒ 00:20:34.280 Emily Giant: for me for Felipe’s job. I’d want them inventoried separately, because just because you have 10 plants doesn’t mean you have 10 pots. You can have 3,000 pots and 10 plants. You can only sell 10. But there are other teams that don’t want to view it like that. So, Felipe, from your 1st of all, do we even want to go to non florals, or do you have? Should we stick with like.
172 00:20:34.880 ⇒ 00:20:35.886 felipefaria: Yeah, the.
173 00:20:36.700 ⇒ 00:20:43.179 felipefaria: I think that in the no floral side it’s gonna be a bigger conversation. Because.
174 00:20:43.470 ⇒ 00:20:55.620 felipefaria: as you said, right like, we used to have the components separated. And then we started just having the in in the final component. Ideally, we would wanna have it
175 00:20:55.940 ⇒ 00:20:58.210 felipefaria: the way that you described kind of like
176 00:20:58.320 ⇒ 00:21:07.549 felipefaria: uploading both components to dash or to netsuite, and then, once an order comes in, it decrements one plant, one pot.
177 00:21:08.185 ⇒ 00:21:18.219 felipefaria: Right now, this is not really how it’s being done right like a a work order is created in netsuite. And then what is showing dash
178 00:21:18.370 ⇒ 00:21:25.980 felipefaria: is really just the final kitted component. We do have the pods available there. But that’s
179 00:21:26.320 ⇒ 00:21:38.790 felipefaria: when we were uploading the pots with the expectation that we were gonna have kind of like the system of decrementation happening. But right now it is not happening. For now.
180 00:21:39.180 ⇒ 00:21:45.510 felipefaria: because I think that this is a larger conversation, because you would have to essentially change
181 00:21:45.760 ⇒ 00:21:56.809 felipefaria: the way that the no floors are sold in. Shopify and and kind of like on the sales side is kind of like, how the E-com team
182 00:21:57.910 ⇒ 00:22:05.080 felipefaria: configures in these products on the website, right? And so I think. And that’s a larger conversation. For now
183 00:22:05.240 ⇒ 00:22:13.550 felipefaria: I would just wanna have the accurate information in terms of sales and on hand in afs of the final kitted
184 00:22:14.340 ⇒ 00:22:21.210 felipefaria: component. Which is yeah. And and and that’s the main thing we would are gonna have to figure it out
185 00:22:22.530 ⇒ 00:22:32.819 felipefaria: in the new floral. I have this actually marked for like summer projects here. But obviously, I think that there are some other priorities. But
186 00:22:32.940 ⇒ 00:22:41.310 felipefaria: we and and I’ll definitely shoot to have this kind of brought up in the next couple of weeks once. I think
187 00:22:41.510 ⇒ 00:22:45.989 felipefaria: the base reporting of everything that we used to have before is kind of fixed
188 00:22:46.550 ⇒ 00:22:54.969 felipefaria: but but yeah, like we would want you, because right now it’s it’s kind of wonky. The way that is happening is we’re managing
189 00:22:55.290 ⇒ 00:23:13.969 felipefaria: the the pots essentially manually on a spreadsheet. Which is, is not a clean way to do it. But if it’s not too much of a lift, because I don’t want you guys to kind of work on a whole system for them to potentially change it later.
190 00:23:15.900 ⇒ 00:23:28.900 felipefaria: I think right now, we do have the information on the known florals, right? And I think that there’s some variance, some discrepancy on it. I haven’t done the validation for last week, but when you share it in that report with me. I did the validation, and the numbers were
191 00:23:29.070 ⇒ 00:23:31.940 felipefaria: close enough. There were some variances, too.
192 00:23:32.980 ⇒ 00:23:36.150 felipefaria: But yeah, like
193 00:23:36.280 ⇒ 00:23:46.909 felipefaria: as I said, for now it’s just focusing on really getting it and getting information on the final final kitted sale. And it would be, I think, the same as the
194 00:23:47.320 ⇒ 00:23:51.769 felipefaria: the hard goods right where you’re just looking at transactions, data, and
195 00:23:52.320 ⇒ 00:23:56.529 felipefaria: and just pulling what was sold. And from that, and from this queue.
196 00:23:56.530 ⇒ 00:23:58.899 Emily Giant: Yeah, you can. You can pull it
197 00:23:59.230 ⇒ 00:24:14.729 Emily Giant: on the lot table because they are allotted. But it doesn’t give you a good view of pot inventory. So if, like your purpose for looking at the dashboard is figuring out how many pots you have, so that you don’t have to use an excel spreadsheet. It’s no good.
198 00:24:15.170 ⇒ 00:24:28.559 felipefaria: Yeah. Yeah. But but that’s and that’s gonna be tricky anyways into. And to get the part inventory. And and that’s just because one is not implementing. Dash right! It’s not like a like a bundle, like a floral bundle where
199 00:24:29.220 ⇒ 00:24:36.779 felipefaria: the customer buys like a vogue bouquet, and the system already decrements. One of the vase, one of the bouquet.
200 00:24:36.880 ⇒ 00:24:40.399 felipefaria: the on the known floral side, is like a work order, so we have
201 00:24:40.580 ⇒ 00:24:44.760 felipefaria: pots in that suite, and then a certain amount of those pods
202 00:24:45.000 ⇒ 00:24:51.780 felipefaria: or transferred into a work order. So technically, it should decrement the
203 00:24:51.990 ⇒ 00:25:08.470 felipefaria: kind of outstanding like available pots. Once you create a work order. But then, to actually have an accurate count of the pots, you would have to get whatever pots that are not in a work order, plus the pots that are in a work order that are still Afs
204 00:25:09.217 ⇒ 00:25:12.980 felipefaria: which I don’t know exactly how that would work.
205 00:25:13.622 ⇒ 00:25:17.719 felipefaria: So so this why ideally in the perfect.
206 00:25:18.410 ⇒ 00:25:25.839 felipefaria: the perfect state of things. We would go without work orders. We would eliminate work orders completely and just treat non florals as
207 00:25:26.100 ⇒ 00:25:36.399 felipefaria: the same way as you treat a floral bundle right where it’s just a pairing, and this is how it is put in the PIN, and this is how it’s sold on the on the website. And yeah.
208 00:25:37.300 ⇒ 00:25:46.980 Emily Giant: That’s like from a data perspective that would be so much better, and even from like a quality assessment perspective, because non florals have the most breaking pots.
209 00:25:47.160 ⇒ 00:25:54.159 Emily Giant: and I don’t know how anyone ever knows what pot is breaking, because, like, it’s just a random skew. So oh, we’re
210 00:25:54.950 ⇒ 00:26:00.420 Emily Giant: sorry, Demo. Lotta, you go ahead. I have a question about like what exactly a work order is. But
211 00:26:00.900 ⇒ 00:26:02.690 Emily Giant: if that’s not helpful for this.
212 00:26:04.015 ⇒ 00:26:12.120 Demilade Agboola: I think for me, I’m just trying to get like a full frame of what? Of what priorities are for Felipe?
213 00:26:14.220 ⇒ 00:26:19.310 Demilade Agboola: So like, what numbers do you need in terms of
214 00:26:19.940 ⇒ 00:26:23.699 Demilade Agboola: like to get your job done on a daily on a weekly?
215 00:26:24.050 ⇒ 00:26:25.799 Demilade Agboola: What numbers do you need
216 00:26:27.512 ⇒ 00:26:33.630 Demilade Agboola: those numbers and like, what is the final step for us to get the phone numbers.
217 00:26:35.040 ⇒ 00:26:45.679 felipefaria: Well, the the final step. I’m not entirely sure. But right now I wouldn’t focus on the pots too much, as I said in the main thing that I need is just accurate
218 00:26:46.030 ⇒ 00:26:58.210 felipefaria: sales information, and ideally, I would wanna like. And I would need that information broken down right by what is actually sales? What is subscription? What is redelivery?
219 00:26:59.061 ⇒ 00:27:05.240 felipefaria: And for all the categories so floral, non-floral, and hard goods?
220 00:27:06.022 ⇒ 00:27:12.360 felipefaria: And then that information should be able to be broken down. However, I want right, like by Fc.
221 00:27:12.530 ⇒ 00:27:17.740 felipefaria: And by any week that I want to look at essentially.
222 00:27:17.890 ⇒ 00:27:20.170 felipefaria: And so the main thing is just like
223 00:27:20.930 ⇒ 00:27:26.249 felipefaria: and and and that’s the base of it all. And for me, after that
224 00:27:26.770 ⇒ 00:27:37.509 felipefaria: there were other reports that I think, like you know, I already mentioned to to Emily one being kind of like, you know the information of the sub or the suborders by component and piece
225 00:27:37.800 ⇒ 00:27:42.690 felipefaria: by map type. And that’s kind of like an important one.
226 00:27:44.290 ⇒ 00:27:50.529 felipefaria: But the core one is just really having accurate information on those
227 00:27:53.350 ⇒ 00:27:58.328 felipefaria: After that, that’s why we? I think Emily is already discussing just
228 00:27:58.980 ⇒ 00:28:15.019 felipefaria: transferring the adjustments that are made the Ias into looker in an accurate manner, so we can see kind of like. How? What was the spoilage? What was the shrinkage and stuff like this which right now I am pulling from Netsuite. So I’m still getting the information accurately. It’s just
229 00:28:16.009 ⇒ 00:28:26.340 felipefaria: ideally. We would have that in looker as well for dashboard purposes. And so everybody has the visibility into these numbers.
230 00:28:26.920 ⇒ 00:28:33.910 felipefaria: because nobody. It’s not everybody that is kind of familiar with netsuite and the pro. The process to get that information from Netsuite is not.
231 00:28:34.240 ⇒ 00:28:38.950 felipefaria: It’s not very straightforward. Right? So I think we’re
232 00:28:39.150 ⇒ 00:28:42.509 felipefaria: we’re getting there. I just wanna make sure that
233 00:28:43.350 ⇒ 00:28:47.470 felipefaria: as you guys are working, releasing this in this looker files.
234 00:28:47.820 ⇒ 00:28:58.250 felipefaria: I’m working with you guys on validating it and flagging sort of issues that I’m finding, because then I can go back to Dash and
235 00:28:58.790 ⇒ 00:29:07.739 felipefaria: just make sure that the numbers are matching with in with dash. Essentially and I don’t know if you guys
236 00:29:07.960 ⇒ 00:29:09.239 felipefaria: oh, sorry! Go ahead.
237 00:29:09.400 ⇒ 00:29:10.370 Emily Giant: No go ahead! Go ahead!
238 00:29:10.687 ⇒ 00:29:25.912 felipefaria: No, I I was just gonna say, I don’t know if you guys have been working on the base sales data, which is well, and right now where? Where? I’m pulling the the hard goods information, right? Because and and I was checking this morning and
239 00:29:27.530 ⇒ 00:29:36.020 felipefaria: like, and I pulled the information of sales for La for last week, for example, and it was showing the glass centerpiece face.
240 00:29:36.390 ⇒ 00:29:39.099 Emily Giant: But now, for some reason.
241 00:29:39.954 ⇒ 00:29:43.819 felipefaria: Is not showing the glass centerpiece.
242 00:29:44.450 ⇒ 00:29:52.524 Emily Giant: Oh, alright! I like nearly died fixing base sales data like 2 weeks ago for the hard goods.
243 00:29:52.930 ⇒ 00:30:06.039 felipefaria: And I pulled it yesterday, though, and it was there, and now and now it’s not, and the glass into piece the melody is just is one of those products that are a bundle and that I was talking about so it can only be sold
244 00:30:06.280 ⇒ 00:30:09.689 felipefaria: when the customer purchase a specific bouquet. Essentially
245 00:30:09.950 ⇒ 00:30:16.829 felipefaria: so. And we had issues in the past kind of like with that information being accurate. For some reason. Now, Emily, like it’s
246 00:30:19.150 ⇒ 00:30:22.900 felipefaria: I don’t know why. It’s just not not there.
247 00:30:24.000 ⇒ 00:30:28.110 Emily Giant: It’s I have no idea, because the only thing that we have
248 00:30:28.320 ⇒ 00:30:38.459 Emily Giant: deployed is I deployed like a new fiscal calendar which shouldn’t affect anything because we had a 53 week year last year, so all of our comps were off
249 00:30:39.128 ⇒ 00:30:49.519 Emily Giant: and then demolates didn’t touch. So I’ll look into that. But I’m just gonna open a ticket same from sales today. So can you see my screen here where it says for ranking by Felipe.
250 00:30:51.590 ⇒ 00:30:53.070 Emily Giant: am I sharing my screen.
251 00:30:53.070 ⇒ 00:30:53.840 felipefaria: Yeah, yeah.
252 00:30:53.840 ⇒ 00:30:54.540 Demilade Agboola: Yes.
253 00:30:54.540 ⇒ 00:30:58.049 Emily Giant: Okay, am I missing any looker? Dashboards that need to be
254 00:30:58.540 ⇒ 00:31:03.439 Emily Giant: fixed, or it’s not even fixed per se. But like we’re looking for the top.
255 00:31:05.960 ⇒ 00:31:16.669 Emily Giant: dashboards! You use that contain this data so that, like once it’s done, we can swap it all out. But I feel like I’m still missing a couple I know.
256 00:31:16.670 ⇒ 00:31:17.799 felipefaria: And this is.
257 00:31:18.900 ⇒ 00:31:19.230 Emily Giant: Go ahead!
258 00:31:19.230 ⇒ 00:31:24.000 felipefaria: Okay. And this is strictly dashboards. Right? Not looks. Necessarily.
259 00:31:24.000 ⇒ 00:31:25.939 Emily Giant: Looks, too, would probably be good.
260 00:31:25.940 ⇒ 00:31:26.650 felipefaria: What do you think?
261 00:31:26.650 ⇒ 00:31:30.439 Emily Giant: Because Flip, it deals a lot in looks instead of dashboard.
262 00:31:30.440 ⇒ 00:31:31.160 Demilade Agboola: Oh!
263 00:31:31.480 ⇒ 00:31:31.930 Emily Giant: Yeah.
264 00:31:31.930 ⇒ 00:31:37.590 felipefaria: Yeah, I look mainly at looks, and I’m and I’m just pulling the information right? I have like a look for
265 00:31:37.970 ⇒ 00:31:48.199 felipefaria: for sale like floral sales by Fc. Total or floral sales by week, and floral sales like, and then Hargoods total Hargoods by week.
266 00:31:48.787 ⇒ 00:31:51.649 felipefaria: Like just variations of the same.
267 00:31:51.650 ⇒ 00:31:52.030 Demilade Agboola: And.
268 00:31:52.030 ⇒ 00:31:54.670 felipefaria: But formatted in different ways, essentially.
269 00:31:55.370 ⇒ 00:31:57.430 Demilade Agboola: Oh, do you? Do you build out the looks yourself.
270 00:31:58.452 ⇒ 00:32:04.960 felipefaria: Yeah, yeah, I’ve been building out some looks, and from the and from like and from the
271 00:32:06.070 ⇒ 00:32:10.670 felipefaria: table stain that Emily direct me to right so and for
272 00:32:11.150 ⇒ 00:32:18.779 felipefaria: for targets is the is a combination right? It’s like base sales data, but a component in the components itself.
273 00:32:18.990 ⇒ 00:32:25.530 felipefaria: And then for the floral one is like all the the polyatomic new tables that were created.
274 00:32:27.260 ⇒ 00:32:32.330 Emily Giant: All right. So I’m I’m gonna put this one up top.
275 00:32:34.180 ⇒ 00:32:37.760 felipefaria: And I can send kinda like, if you guys want, I can send the link to to.
276 00:32:37.760 ⇒ 00:32:38.380 Emily Giant: Yeah.
277 00:32:38.380 ⇒ 00:32:40.660 felipefaria: In all of these books that I created.
278 00:32:41.050 ⇒ 00:32:47.739 Emily Giant: If you can just save the links. In the brain, forge dashboard under Felipe’s looks.
279 00:32:48.140 ⇒ 00:32:49.149 felipefaria: This looks like.
280 00:32:49.150 ⇒ 00:32:55.620 Emily Giant: Like a rolodex of like outfits you wore. It’s like Felipe’s looks anyway. Yeah, like.
281 00:32:55.620 ⇒ 00:33:02.419 felipefaria: Can you send me the link to this? And I don’t know if I if I’ve received the link to this via email.
282 00:33:02.800 ⇒ 00:33:04.499 felipefaria: Oh, this is in Looker.
283 00:33:04.820 ⇒ 00:33:15.529 Emily Giant: Yeah. So if you all you do is like, click, add content, and then pop the URL link right here. And it will pre populate it with all of the
284 00:33:15.740 ⇒ 00:33:22.409 Emily Giant: the the boards that you use. So I think I have most of your dashboards here.
285 00:33:23.830 ⇒ 00:33:28.029 Emily Giant: And also here the supply chain ones
286 00:33:29.810 ⇒ 00:33:36.309 Emily Giant: to the top demo all day and like write inventory, because I know it’s not as clear as it was like 4 months ago when we made this board
287 00:33:36.490 ⇒ 00:33:42.960 Emily Giant: inventory, and these are definitely more inventory.
288 00:33:45.710 ⇒ 00:33:57.269 felipefaria: Yeah, and I’ll just put I’ll just pop all the and all the looks that I have there. The one thing that we might wanna add to that list that you had, Emily, is just
289 00:33:57.400 ⇒ 00:34:01.190 felipefaria: that measure. That kind of has
290 00:34:01.390 ⇒ 00:34:08.219 felipefaria: everything that shipped out, combined and unnecessarily separated by sales for delivery and subscriptions.
291 00:34:08.370 ⇒ 00:34:09.730 Emily Giant: Hmm, yeah.
292 00:34:09.739 ⇒ 00:34:20.859 felipefaria: Because sometimes I just want the whole number of what was shipped right and and another thing that I was thinking about, and something that is occurring more often now is
293 00:34:22.239 ⇒ 00:34:26.129 felipefaria: marketing sense. Essentially
294 00:34:27.529 ⇒ 00:34:39.919 felipefaria: I don’t know exactly how those orders are being reflected in these reports, because if I go to dash. I see that they have a tag like a marketing tag on it, and even the order number looks different
295 00:34:40.614 ⇒ 00:34:45.355 felipefaria: and just like the formatting of it looks different. So I don’t know.
296 00:34:46.249 ⇒ 00:34:48.724 felipefaria: if there is a way
297 00:34:50.159 ⇒ 00:34:54.729 felipefaria: like to just validate whether those are falling under sales.
298 00:34:55.169 ⇒ 00:35:07.299 felipefaria: or if they’re being omitted altogether like I don’t like. I don’t necessarily know, because we we do have some variances in the reconciliation that I do on a weekly basis. But those variances are within
299 00:35:07.589 ⇒ 00:35:13.619 felipefaria: the the threshold that we that we allow, which is kind of like an overall, 2% variance.
300 00:35:13.919 ⇒ 00:35:24.279 felipefaria: But it’s still like it’s a hundred 20 units various, you know, like that. I’m suspecting that could be a few different reasons for it.
301 00:35:24.569 ⇒ 00:35:34.569 felipefaria: One of the reasons could be this marketing sense, and I can send you, if you want a list of kind of like.
302 00:35:34.570 ⇒ 00:35:43.780 Emily Giant: Yeah, that would be great, because he’s kind of operating without, like the
303 00:35:43.890 ⇒ 00:35:53.870 Emily Giant: examples that we see that make it easier to understand. But he’s definitely worked in marketing and corporate quantity into, like the intermediate table that is.
304 00:35:54.560 ⇒ 00:35:54.950 Demilade Agboola: You know.
305 00:35:54.950 ⇒ 00:36:02.399 Emily Giant: Source of truth. But I don’t have dates in this model. So
306 00:36:03.180 ⇒ 00:36:06.330 Emily Giant: that would be great to be able to pull like
307 00:36:06.550 ⇒ 00:36:12.570 Emily Giant: the inventory number related to it, I’d be able to walk it back, but, like.
308 00:36:12.970 ⇒ 00:36:14.250 felipefaria: That is an adjustment.
309 00:36:14.250 ⇒ 00:36:31.699 Emily Giant: But I don’t know if it’s working like we have several adjustment types that are still active in netsuite. For example, care is meant to be like the redelivery tag. And that’s what was true before we migrated to shopify. Yeah, this is showing 0, even though it’s an active adjustment type. It’s like, not
310 00:36:32.520 ⇒ 00:36:37.530 Emily Giant: yeah, intact from dash, which is a source issue. So I can.
311 00:36:40.350 ⇒ 00:36:48.710 felipefaria: And I just sent you guys a link to some orders that were posted. I know that they were posted for delivery this week.
312 00:36:49.010 ⇒ 00:36:55.500 felipefaria: and and you can see kind of like the order number starts with A, B, and then it’s like B, and then a bunch of numbers.
313 00:36:56.091 ⇒ 00:37:04.350 felipefaria: It would be interesting just to see kinda if those are included in the
314 00:37:05.120 ⇒ 00:37:09.089 felipefaria: mainly in this table that I’m using to pull the floral
315 00:37:09.450 ⇒ 00:37:16.240 felipefaria: sales, which is hold on a second, just to give you guys the exact table.
316 00:37:21.770 ⇒ 00:37:23.629 Emily Giant: These Levain orders.
317 00:37:24.100 ⇒ 00:37:33.580 Emily Giant: They found out that we can’t cancel them in our system, which is super fun, just an aside, like if something can go wrong with like a new product, it will.
318 00:37:36.900 ⇒ 00:37:45.300 felipefaria: The the inventory balance. One right. I think that this is inventory or inventory adjustments. Actually sorry inventory adjustments is the
319 00:37:46.200 ⇒ 00:37:51.850 felipefaria: the main table that we’re using for the floral floral sales. Things like that.
320 00:37:52.120 ⇒ 00:37:52.540 Emily Giant: Okay.
321 00:37:52.540 ⇒ 00:38:00.580 felipefaria: For me, I mean ideally, we would have those separated if they are under the sales
322 00:38:01.010 ⇒ 00:38:07.680 felipefaria: like, for now, finally, those are being accounted for but
323 00:38:07.810 ⇒ 00:38:12.080 felipefaria: it would be nice to have kind of like another
324 00:38:12.380 ⇒ 00:38:19.720 felipefaria: another kind of category that, and that I can have a look just for like marketing sales right? And then.
325 00:38:19.940 ⇒ 00:38:23.680 felipefaria: when we have that combined measure of everything that shipped out
326 00:38:23.960 ⇒ 00:38:32.129 felipefaria: marketing sense would be included in that, so it would be like floral, plus subscriptions plus redelivery plus marketing.
327 00:38:32.320 ⇒ 00:38:34.640 felipefaria: which those would be.
328 00:38:35.000 ⇒ 00:38:43.719 felipefaria: I believe, all of the possible ways that a product would ship out there could be
329 00:38:44.400 ⇒ 00:38:48.220 felipefaria: samples, but I don’t think that samples.
330 00:38:51.160 ⇒ 00:38:57.839 Emily Giant: That’s not an all of the active adjustment types, let me see. So wait on the subject.
331 00:38:58.550 ⇒ 00:39:04.519 Emily Giant: Demo. A. I think he touched on a point that I am.
332 00:39:05.550 ⇒ 00:39:10.899 Emily Giant: Our team is used to seeing the inventory lot balance and the adjustments
333 00:39:11.020 ⇒ 00:39:17.849 Emily Giant: or transactions rather separated. That’s why I built them out that way. Initially, I don’t think it’s necessary
334 00:39:18.080 ⇒ 00:39:40.590 Emily Giant: as long as we give good documentation. I think so. What he’s been doing is like, instead of having the 2 separate tables, and the reason Stephen and I both did it that way is because of fan out so like, remember, when you were doing Qa. And you were like, well, it’s showing like 10 million sales in this table. When I pull this number. It’s because for every order it was like a multiplying
335 00:39:41.000 ⇒ 00:39:45.990 Emily Giant: every sale on the lot times the number. But Demo Latte is like
336 00:39:46.120 ⇒ 00:40:06.530 Emily Giant: better at this than Steven and I, so he’s able to like. Make it not fan out, and only have one source of truth where you could pull every sale at the suborder level in addition to the inventory number level? Do you think that there’s a risk given that he’s been able to like?
337 00:40:07.506 ⇒ 00:40:11.800 Emily Giant: Normalize all that data with combining those features
338 00:40:12.470 ⇒ 00:40:19.329 Emily Giant: for the teams? Or would it be fine to have it all in one data set as long as we set up the dashboards in a way that
339 00:40:19.490 ⇒ 00:40:22.110 Emily Giant: it kind of retrained. How we think about it.
340 00:40:26.150 ⇒ 00:40:35.450 felipefaria: And you’re talking about having all in one data set you’re talking about, like all of the adjustments, or like like what exactly would be, you know, in all the the
341 00:40:35.710 ⇒ 00:40:37.019 felipefaria: to data, science.
342 00:40:37.020 ⇒ 00:40:44.120 Emily Giant: Look at every adjustment type, everything that went out the door, in addition to like sub order, level granularity.
343 00:40:44.830 ⇒ 00:41:05.289 Emily Giant: So you’d be able to pull like a row that had a suborder id. But in that same row, if you wanted to see the number of total sales that came from that lot. It would be there, too. Now, I don’t know why you’d ever want to look at that, but that was the best example I could think of on the spot like, that’s what we’re talking about, right, Demo. A like being able to like, cleanly roll up
344 00:41:05.620 ⇒ 00:41:16.429 Emily Giant: into like aggregate sales from the suborder level or from the item level, not even suborder item per suborder level, rolling up into like all aggregate.
345 00:41:17.560 ⇒ 00:41:22.220 Demilade Agboola: Yeah, I I want us to have a table that has for each sub order
346 00:41:22.590 ⇒ 00:41:28.039 Demilade Agboola: and each item that makes up that supporter. We can have an idea of like the quantity
347 00:41:29.110 ⇒ 00:41:31.999 Demilade Agboola: we can have a value for the quantity such that
348 00:41:32.510 ⇒ 00:41:34.080 Demilade Agboola: where we want to roll up
349 00:41:34.300 ⇒ 00:41:37.590 Demilade Agboola: that allows us to roll up in multiple ways.
350 00:41:41.730 ⇒ 00:41:48.969 Demilade Agboola: So like right like right now, if I share my screen, which is the the table, I.
351 00:41:49.480 ⇒ 00:41:59.300 Emily Giant: Yeah, I think it’s better if you. I’m gonna mute myself and demolata you drive, cause I think that what you’re what you’ve built is what he’s saying he wants so.
352 00:41:59.780 ⇒ 00:42:02.370 Demilade Agboola: Yeah, so like this is.
353 00:42:02.480 ⇒ 00:42:09.910 Demilade Agboola: I know you mentioned, like everything shipped out so total in this, not in this context. Total quantity ship sold will be everything that was shipped out.
354 00:42:10.590 ⇒ 00:42:16.760 Demilade Agboola: So we have the total subscription quantity, total redelivery quantity, and total sale quantity.
355 00:42:17.250 ⇒ 00:42:19.939 Demilade Agboola: But then it’s also broken down to committed.
356 00:42:20.250 ⇒ 00:42:22.540 Demilade Agboola: So this is the commentary quantity sold.
357 00:42:23.566 ⇒ 00:42:26.050 Demilade Agboola: This is the committed quantity
358 00:42:26.170 ⇒ 00:42:28.680 Demilade Agboola: subscription quantity. So you can also see
359 00:42:29.654 ⇒ 00:42:36.620 Demilade Agboola: which of the items that belong to that inventory. Id like that lot were sold
360 00:42:37.030 ⇒ 00:42:41.840 Demilade Agboola: for a subscription for a delivery versus like the regular sale.
361 00:42:42.170 ⇒ 00:42:49.259 Demilade Agboola: and then the uncommitted quantities as well for both, like hotel shipped like the unlimited quantity, sold
362 00:42:49.890 ⇒ 00:42:54.229 Demilade Agboola: the uncommitted subscription redelivery sale.
363 00:42:54.350 ⇒ 00:43:01.760 Demilade Agboola: And then we have, like the different adjustments based off that inventory number. So we have the spoilage quantity, the shrinkage quantity.
364 00:43:01.970 ⇒ 00:43:11.700 Demilade Agboola: The system is much quantity receiving rejected average logistics, career and marketing, which we might have to look into, because, like marketing seems to be due for every single thing
365 00:43:12.060 ⇒ 00:43:12.940 Demilade Agboola: on.
366 00:43:13.500 ⇒ 00:43:21.629 Demilade Agboola: Or maybe we’re just like they’re just meant to just not marking it the way we think it is, and there’s a different like way to find that in our data.
367 00:43:22.333 ⇒ 00:43:28.350 Demilade Agboola: But the idea is, everything will leave error against inventory. Id.
368 00:43:28.630 ⇒ 00:43:30.379 Demilade Agboola: we can kind of see it.
369 00:43:30.500 ⇒ 00:43:39.799 Demilade Agboola: And then potentially what we might end up doing is for every eventually I number Id. We can further break this down into the sub order Ids
370 00:43:40.740 ⇒ 00:43:47.390 Demilade Agboola: on that inventory number, so that so that we can always roll it up back to this form.
371 00:43:48.850 ⇒ 00:43:57.510 Demilade Agboola: I kind of see also total constancy sold is 34. But the 34 was for each of these like. So borders was maybe 2 here
372 00:43:57.710 ⇒ 00:43:58.390 Demilade Agboola: 5 years.
373 00:43:58.390 ⇒ 00:44:01.250 Demilade Agboola: Yeah, that kind of thing.
374 00:44:02.430 ⇒ 00:44:15.820 felipefaria: Yeah, yeah, no, I I’m I’m fine with this. With this format, they would just consolidate everything to one and then, obviously, we can create looks. We just pull specific tables like specific columns. And from
375 00:44:16.000 ⇒ 00:44:19.560 felipefaria: like, from this right? My question is
376 00:44:19.760 ⇒ 00:44:26.679 felipefaria: we, we would still be able to get this information by by skew and by location.
377 00:44:28.910 ⇒ 00:44:31.160 Demilade Agboola: Yes, we should be able to.
378 00:44:32.050 ⇒ 00:44:35.650 Demilade Agboola: Because we have the inventory number. Id. We can always.
379 00:44:35.940 ⇒ 00:44:36.670 felipefaria: Yeah, that’s correct.
380 00:44:36.670 ⇒ 00:44:37.880 Demilade Agboola: Location Yeah.
381 00:44:38.030 ⇒ 00:44:42.170 felipefaria: The inventory number. Id. Is that the lot? And that’s a lot right.
382 00:44:42.170 ⇒ 00:44:43.740 Demilade Agboola: Lot. That’s lot 80.
383 00:44:44.450 ⇒ 00:44:47.710 felipefaria: Yeah. Yeah, I I mean.
384 00:44:47.890 ⇒ 00:44:55.729 felipefaria: whichever way makes the most sense to me. I I remember before, like, when Steven was still in here.
385 00:44:56.050 ⇒ 00:44:57.739 felipefaria: We used to have
386 00:44:58.580 ⇒ 00:45:03.929 felipefaria: a table like that, and like this, that you should have kind of like all of the
387 00:45:04.110 ⇒ 00:45:17.489 felipefaria: all of the adjustment types. And we could. We could also see the subscription. I think it was the. It was the inventory transactions, except table, right? And then there was like the it was called the expense
388 00:45:17.630 ⇒ 00:45:22.540 felipefaria: type in that in that column, and it had all sorts of
389 00:45:23.060 ⇒ 00:45:45.109 felipefaria: all sorts of adjustments. Right? We deliver response care marketing and all this sort of stuff. So we can. And we can do it like this. This is kind of like the same thing that I used to use in the past. Really? The one thing that I would wanna confirm that it. This, I think, is more of a process thing, is like
390 00:45:46.340 ⇒ 00:45:53.779 felipefaria: checking with care. How how this kinda
391 00:45:54.240 ⇒ 00:46:04.899 felipefaria: units that ship that are not sales redelivery or subscription. How are they actually input it into the system. Just so. They are
392 00:46:05.090 ⇒ 00:46:14.089 felipefaria: reflected here correctly. Right? I’m not entirely sure right now, like the marketing orders. Are they being tagged as
393 00:46:15.040 ⇒ 00:46:18.207 felipefaria: as a marketing and
394 00:46:19.450 ⇒ 00:46:26.019 felipefaria: marketing in a well. How is it called? Here in this is is marketing in something right marketing.
395 00:46:26.020 ⇒ 00:46:27.500 Demilade Agboola: Yeah, yeah, right?
396 00:46:27.500 ⇒ 00:46:30.919 Demilade Agboola: Yeah. And and I don’t know, because this for me is like.
397 00:46:31.910 ⇒ 00:46:41.560 felipefaria: I guess all of them are adjustment types. But I’m just not sure if they are putting that as marketing in corporate or something else.
398 00:46:42.101 ⇒ 00:46:51.498 felipefaria: So this is something like, I’ll make a note here just to check with care like on this marketing sense. So for all this,
399 00:46:52.910 ⇒ 00:47:06.659 felipefaria: kinda levain initiatives like when we send product to influencers and and things like that. But not only that, but also samples. For example, right? I think that sometimes we do
400 00:47:07.000 ⇒ 00:47:17.830 felipefaria: with the quality sense. So some people in the company will receive bouquets every every month or every week to to check on the quality.
401 00:47:18.210 ⇒ 00:47:22.090 felipefaria: I’m not entirely sure how those are being shipped out like under what
402 00:47:22.510 ⇒ 00:47:27.410 felipefaria: adjustment type they are being shipped out, whether it’s sales or
403 00:47:28.000 ⇒ 00:47:32.284 felipefaria: or something else that might not be reflected here.
404 00:47:33.290 ⇒ 00:47:40.719 felipefaria: It could be something like a care adjustment that I I don’t see it here that we have a care adjustment, right?
405 00:47:40.890 ⇒ 00:47:59.180 felipefaria: So I don’t know how they are. They are putting this this. This quantities in again. It’s like should be a small fraction of the overall sales for any given week, but I think we would want to have visibility on that as well. But this format works for me.
406 00:48:00.100 ⇒ 00:48:08.989 Demilade Agboola: Okay, that’s that’s great to hear. Yeah. The care. This, the care and the care buffers are like another table.
407 00:48:09.120 ⇒ 00:48:15.630 Demilade Agboola: and we’ll, you know, bring them together, based off the inventory number. Id, so that will tie perfectly
408 00:48:19.130 ⇒ 00:48:22.430 felipefaria: Yeah, the thing is the uncommitted right? That is kind of tricky
409 00:48:23.520 ⇒ 00:48:30.590 felipefaria: like, how do we have the uncommitted? In the 1st place? We would like.
410 00:48:31.690 ⇒ 00:48:36.780 felipefaria: I guess that I could use the total quantity sold.
411 00:48:38.274 ⇒ 00:48:43.895 felipefaria: Total quantity sold would include the committed and noncommitted
412 00:48:45.200 ⇒ 00:48:45.960 Demilade Agboola: Yes.
413 00:48:47.030 ⇒ 00:48:52.619 felipefaria: Yeah, ideally, if we could have kind of like this mesh right of
414 00:48:52.900 ⇒ 00:48:58.419 felipefaria: so total quantity sold is everything that went out the door. And then.
415 00:48:58.420 ⇒ 00:48:59.070 Demilade Agboola: Yes.
416 00:48:59.450 ⇒ 00:49:12.409 felipefaria: If we have a measure for subscription with delivery and sale quantity that shows committed plus uncommitted
417 00:49:12.570 ⇒ 00:49:21.280 felipefaria: as well. Just use just because I and I would essentially want to
418 00:49:22.500 ⇒ 00:49:30.800 felipefaria: break down the total quantity sold into these categories, regardless of whether it’s committed or uncommitted. If that makes sense.
419 00:49:31.270 ⇒ 00:49:32.220 felipefaria: Oh.
420 00:49:32.220 ⇒ 00:49:34.379 Demilade Agboola: I’m not sure I followed. To be honest.
421 00:49:35.150 ⇒ 00:49:36.140 felipefaria: So.
422 00:49:36.390 ⇒ 00:49:42.290 felipefaria: and when I’m doing the the reconciliations, and for the week, right like, and I and I would pull.
423 00:49:42.430 ⇒ 00:49:48.939 felipefaria: and I would pull what was under sales, what was under redelivery, and what was under subscription.
424 00:49:49.570 ⇒ 00:49:50.300 Demilade Agboola: Okay.
425 00:49:50.300 ⇒ 00:49:56.149 felipefaria: Right now in this example. Right? I’m looking at the 1st row here, and I would pull this
426 00:49:56.820 ⇒ 00:50:07.830 felipefaria: well. The total quantity sold is 34. So there’s 0 subscriptions, 3 with deliveries and 31 sales. So okay, so this makes up 34.
427 00:50:08.020 ⇒ 00:50:08.680 felipefaria: Okay.
428 00:50:08.680 ⇒ 00:50:09.190 Demilade Agboola: Yeah.
429 00:50:09.190 ⇒ 00:50:12.160 felipefaria: And then, okay, so follow and then
430 00:50:12.160 ⇒ 00:50:15.340 felipefaria: committed. Oh, so so it’s already rolled into so.
431 00:50:15.340 ⇒ 00:50:15.690 Demilade Agboola: Yeah.
432 00:50:15.690 ⇒ 00:50:19.330 felipefaria: Quantities lower than that. Okay, you know. And so it’s fine. So it’s fine.
433 00:50:19.330 ⇒ 00:50:22.150 Demilade Agboola: And then the uncommitted is 4, so the uncommitted.
434 00:50:22.150 ⇒ 00:50:22.580 felipefaria: Perfect.
435 00:50:22.580 ⇒ 00:50:25.310 Demilade Agboola: Will be equal to the total, which is 34.
436 00:50:25.770 ⇒ 00:50:29.539 felipefaria: Okay, yeah. No. Okay. That works that works. Then. Sorry.
437 00:50:30.080 ⇒ 00:50:40.060 Demilade Agboola: Yeah, so yeah, that’s just the basic idea where we can break it down by committed and uncommitted for the different like sale categories. And then we also have the
438 00:50:41.082 ⇒ 00:50:42.930 Demilade Agboola: adjustment types as well.
439 00:50:44.000 ⇒ 00:50:44.740 felipefaria: Okay.
440 00:50:45.140 ⇒ 00:50:58.431 felipefaria: And while you guys are working on this with the current tables that I use be impacted by in any way. And I’m talking specifically about like the inventory adjustments table
441 00:50:59.320 ⇒ 00:51:04.030 felipefaria: which is where I’m pulling right now, right? The sales units that we delivery units and stuff like that.
442 00:51:04.280 ⇒ 00:51:05.170 Demilade Agboola: And so.
443 00:51:05.170 ⇒ 00:51:20.230 Demilade Agboola: but this is an entirely parallel system to it. So the idea is, this is which is part of also part of discussion we’re having about, how do we integrate and get these numbers into your like dashboards? And what’s your daily workflow.
444 00:51:21.139 ⇒ 00:51:25.720 felipefaria: Because we need to be able to figure out like what dashboards do. We need to tweak.
445 00:51:26.190 ⇒ 00:51:29.240 Demilade Agboola: Or do we just need to rebuild entire dashboards
446 00:51:29.400 ⇒ 00:51:31.830 Demilade Agboola: so that you can start to integrate these numbers.
447 00:51:32.390 ⇒ 00:51:33.760 felipefaria: Yeah, like.
448 00:51:34.870 ⇒ 00:51:36.220 felipefaria: And and
449 00:51:37.460 ⇒ 00:51:52.379 felipefaria: for me, as as we already discussed, like I I know that there are dashboards that are used more company wide, and I think that is mainly to provide visibility to everybody. It’s like the Daily order sent right and the performance sent. Those are kind of like the 2 main ones.
450 00:51:52.690 ⇒ 00:52:01.419 felipefaria: Aside from that, we might be building some ad hoc dashboards as needed, but I mainly will look at the
451 00:52:01.640 ⇒ 00:52:03.833 felipefaria: it looks for now.
452 00:52:05.050 ⇒ 00:52:15.510 felipefaria: so as long as I have the raw data source correct, and I can pull and build whatever I want. It’s it’s good by me. And yeah, like, I think.
453 00:52:15.670 ⇒ 00:52:18.811 felipefaria: dashboard wise. That list that you have.
454 00:52:19.870 ⇒ 00:52:24.230 felipefaria: seems to be kind of like, you know the the main reports that we use.
455 00:52:25.530 ⇒ 00:52:30.400 Emily Giant: Okay? So yeah, if you add those looks that you use to that dashboard. That’s perfect.
456 00:52:30.997 ⇒ 00:52:35.472 felipefaria: I created a ticket, Demo latte, so that I can
457 00:52:37.060 ⇒ 00:52:51.800 Emily Giant: Check whether I know we have the adjustment types in the table. But what I don’t know is if they work so that’s not gonna be us. That will be a dev thing if marketing and samples aren’t getting accurately tagged. Post migration.
458 00:52:52.279 ⇒ 00:52:56.229 Emily Giant: So I’ll let you know on the ticket, but that’s documented.
459 00:53:00.120 ⇒ 00:53:06.559 Demilade Agboola: Are also with these numbers that currently exist. Like I said, we’re going to the next step.
460 00:53:06.960 ⇒ 00:53:14.260 Demilade Agboola: I know there’s some edge cases that we still need to account for. Like you know, the marketing is one I know. Consult orders is another
461 00:53:14.850 ⇒ 00:53:25.670 Demilade Agboola: so that those edge cases were accounting for. But largely, these numbers like hold up to the numbers, the raw numbers, and what’s happening in your data?
462 00:53:25.810 ⇒ 00:53:27.560 Demilade Agboola: So the question is,
463 00:53:30.530 ⇒ 00:53:33.529 Demilade Agboola: I mean to be fair. We do have a call tomorrow, but the idea is.
464 00:53:33.720 ⇒ 00:53:38.409 Demilade Agboola: we do have numbers, and we have been able to start like producing some of these numbers.
465 00:53:38.570 ⇒ 00:53:41.609 Demilade Agboola: And we should be thinking about integration.
466 00:53:41.730 ⇒ 00:53:47.868 Demilade Agboola: Because, again, like I said, it’s an entirely parallel work stream. So it’s new data sources, new
467 00:53:48.430 ⇒ 00:53:53.809 Demilade Agboola: models and everything. So how do we like their days to get that across to
468 00:53:54.190 ⇒ 00:54:02.999 Demilade Agboola: you? Perry? Pk, whoever really that needs having like that needs those numbers. So you can be able to start like utilizing
469 00:54:03.220 ⇒ 00:54:06.190 Demilade Agboola: the extra information that you you have available.
470 00:54:06.470 ⇒ 00:54:13.600 Demilade Agboola: And also you using it gives us more edge cases where like things are. Just don’t just add up
471 00:54:13.840 ⇒ 00:54:14.490 Demilade Agboola: right like.
472 00:54:14.870 ⇒ 00:54:21.319 Demilade Agboola: even if they’re like 90% there. And there’s a weird number you call up, call it out. We look into it, can tweak it.
473 00:54:21.320 ⇒ 00:54:21.680 felipefaria: Yes.
474 00:54:21.680 ⇒ 00:54:22.759 Demilade Agboola: Gonna make it better.
475 00:54:23.450 ⇒ 00:54:33.299 felipefaria: Yeah, yeah. And I’ll and I’ll send some some more examples. As I’m validating the numbers. I’m finishing up the last week’s recap
476 00:54:34.010 ⇒ 00:54:42.930 felipefaria: one of the things is like the glass vase thing, right, like the the glass centerpiece, and that that I don’t see it anymore. And then.
477 00:54:43.590 ⇒ 00:54:52.720 felipefaria: in general. I’m still seeing that those small variances but it seems like it’s related to canceled units. But again, it could also be related to
478 00:54:53.920 ⇒ 00:55:05.420 felipefaria: some of these marketing, or care, or or things like that. And and I’ll I’ll go through some specific skews in specific Fcs and see whether there’s any
479 00:55:06.730 ⇒ 00:55:11.070 felipefaria: any large variances. If it’s 1 1 unit variance like.
480 00:55:11.440 ⇒ 00:55:15.378 felipefaria: I’m kinda living on the side right now. But if it’s more than that
481 00:55:15.760 ⇒ 00:55:17.840 felipefaria: I’ll let you. I’ll let you guys know.
482 00:55:18.670 ⇒ 00:55:20.159 Demilade Agboola: Okay. Alright. Sounds.
483 00:55:21.160 ⇒ 00:55:23.130 Emily Giant: Best ones, because it’s like.
484 00:55:23.910 ⇒ 00:55:27.439 Emily Giant: even if it’s just one. Sometimes those are the best ones to send, because it’s like.
485 00:55:27.440 ⇒ 00:55:27.890 felipefaria: Okay.
486 00:55:27.890 ⇒ 00:55:30.090 Emily Giant: Thank you, that one. Then.
487 00:55:30.090 ⇒ 00:55:37.861 felipefaria: Okay, yeah, yeah, I, I should be able to provide plenty of examples of like. And when the when the information is off by one unit.
488 00:55:38.270 ⇒ 00:55:40.455 felipefaria: and I’ll give you some examples.
489 00:55:42.450 ⇒ 00:55:49.989 Emily Giant: I’m happy to help you, too, like, if you need help doing. Qa tag me in and I can, and do that as well.
490 00:55:49.990 ⇒ 00:56:04.990 felipefaria: Should be fine. It doesn’t take that long if you just narrow down to one skew in one fc, what I’m really doing is just going back to dash and going like each day of the week, and seeing how many orders were actually fulfilled
491 00:56:05.210 ⇒ 00:56:09.549 felipefaria: for that product in each of the in each of those days. Right?
492 00:56:11.800 ⇒ 00:56:14.010 felipefaria: Yeah. And then I think
493 00:56:14.940 ⇒ 00:56:17.699 felipefaria: a topic for a different day is
494 00:56:19.050 ⇒ 00:56:30.750 felipefaria: is how like when we build out some sort of dashboard, or look for shrinkage and spoilage.
495 00:56:33.810 ⇒ 00:56:40.380 felipefaria: I think the tricky piece with that is the dates right? So just knowing.
496 00:56:40.380 ⇒ 00:56:40.830 Emily Giant: Yep.
497 00:56:40.830 ⇒ 00:56:49.759 felipefaria: That a spoilage, regardless of when that adjustment was made. We wanted reference to a specific week.
498 00:56:49.870 ⇒ 00:57:05.240 felipefaria: because sometimes what happens is spoilage is added, as an Ia retroactively in the system. So the the team will go back and they see that there’s still available units in that lot and said, oh, actually, those spoiled! And for some reason we didn’t make the adjustment
499 00:57:05.866 ⇒ 00:57:10.550 felipefaria: that product could have spoiled 2 weeks ago, and
500 00:57:10.720 ⇒ 00:57:18.339 felipefaria: the adjustment was made this week. So we want to make sure that that doesn’t show us this week’s spoil. It just shows as polish
501 00:57:18.640 ⇒ 00:57:26.699 felipefaria: from 2 weeks ago, even if it’s retroactively. If you want to like, go on a on a reporting looker and say, what was this published 2 weeks ago?
502 00:57:26.830 ⇒ 00:57:32.783 felipefaria: That number could have gone up because of an adjustment that was made retracted right?
503 00:57:34.560 ⇒ 00:57:41.420 felipefaria: I worked with Steven a lot on that and I know that there was some
504 00:57:41.700 ⇒ 00:58:02.980 felipefaria: a lot of patches, and, like, you know, conditions that he put on the script in order to assign the shrinkage and the spoilage to the correct week that we want to look at, and it was something related to like the spoilage date of the lot. Right? So if the adjustment date is after the spoilage date of the lot
505 00:58:03.880 ⇒ 00:58:10.929 felipefaria: reference that spoil reference, that adjustment to the week that the product spoiled essentially.
506 00:58:11.492 ⇒ 00:58:16.180 felipefaria: So we were gonna have to do something like that. And for and for those adjustments.
507 00:58:16.920 ⇒ 00:58:28.920 Emily Giant: Do you want me to make some? I Felipe and I had a meeting on this right when Brainforge started, because I knew that this was a pain point. Do you want me to like make a ticket, or anything about like
508 00:58:29.090 ⇒ 00:58:33.150 Emily Giant: spoilage logic like something to just help like right.
509 00:58:34.250 ⇒ 00:58:35.190 felipefaria: Well.
510 00:58:35.350 ⇒ 00:58:49.410 felipefaria: and I would honestly like, and I would reference Emily, to like, if you can backtrack in Looker, and I’ll send you the in the report that that Steven and I work together on. But
511 00:58:49.520 ⇒ 00:59:03.280 felipefaria: if you look at the at that script right like how he how he built it, it should provide a good direction of what would have to be done, and I know that the data sources changed. But.
512 00:59:03.710 ⇒ 00:59:04.660 Emily Giant: The logic happens.
513 00:59:04.660 ⇒ 00:59:05.360 Emily Giant: Take care, Lenny.
514 00:59:06.100 ⇒ 00:59:11.199 Emily Giant: it will still be very useful, because it’s it’s all the condition of when the lot was alive
515 00:59:11.420 ⇒ 00:59:21.129 Emily Giant: and the week relative to like the start and spoil date of the lot. But that will definitely help them a lot. I, since it’s like a business specific
516 00:59:22.180 ⇒ 00:59:23.140 Emily Giant: case.
517 00:59:23.520 ⇒ 00:59:26.337 felipefaria: Yeah, I’m I’m posting here on the chat
518 00:59:26.900 ⇒ 00:59:34.019 felipefaria: and it would be. And this is the inventory. I just inventory transactions.
519 00:59:34.270 ⇒ 00:59:34.950 Emily Giant: Yeah.
520 00:59:34.950 ⇒ 00:59:44.119 felipefaria: Xf that I I don’t think it’s working anymore. But hopefully, you guys can look at the at the back end right and see what it was and is the sections
521 00:59:44.240 ⇒ 00:59:56.439 felipefaria: referencing specifically to spoilage and shrinkage, and for this, that should have all this logic built in by Stephen essentially.
522 00:59:56.820 ⇒ 01:00:11.133 Emily Giant: And on that note right before you popped on. I have to. I have to hop in like one second, but I do want to touch on, maybe even separately, from Demo Audit at some point, or you and I can do it. Async Felipe. One of the things I brought up with Demo audit was like.
523 01:00:11.560 ⇒ 01:00:15.450 Emily Giant: how different questions are going to like
524 01:00:15.570 ⇒ 01:00:43.989 Emily Giant: need different fields on that table shrinkage and spoilage right now. Not spoilage, but shrink and system mismatch are going to have adjustments for orders that didn’t properly claim. So those numbers are going to always balance the lot back to 0. But they’re not actually system mismatch. They’re not actually shrink because we actually sent the thing out the door. And
525 01:00:44.140 ⇒ 01:00:50.619 Emily Giant: what we want to do is make sure to start by representing exactly what’s in the system, because that is what
526 01:00:51.000 ⇒ 01:01:05.010 Emily Giant: like we have said physically happened, or what we thought did. But when I backtrack into those shrinkage numbers. I can see that, like 3 of the orders were actually sent, and that that wasn’t shrink. And to me, that throws off accounting, and that throws off.
527 01:01:05.250 ⇒ 01:01:05.770 felipefaria: Yeah.
528 01:01:06.300 ⇒ 01:01:16.710 Emily Giant: So we can talk, maybe at some other point. Once we’ve nailed down exactly the reflection of the system. How we can work through that logic
529 01:01:17.260 ⇒ 01:01:37.860 Emily Giant: in the future to have a true representation of orders that went out the door. His numbers currently show the exact numbers that went out the door in addition to the shrink number. So like where there’s an order that claim, there’s also an adjustment for system mismatch, and that to me, is.
530 01:01:37.860 ⇒ 01:01:38.620 felipefaria: How.
531 01:01:39.440 ⇒ 01:01:49.489 felipefaria: but but an adjustment, either a shrinkage, adjustment, or a system. Mismatch adjustment is not tied to a specific order. Id, is it.
532 01:01:50.980 ⇒ 01:01:57.309 felipefaria: Yeah. And so you. And so you’re saying that some of these adjustments actually shipped out because.
533 01:01:57.620 ⇒ 01:01:58.120 Emily Giant: Many.
534 01:01:58.120 ⇒ 01:02:08.999 felipefaria: There is a gap like there, and there’s essentially 2 more adjustments than what the lot had like. The total quantity of the lot.
535 01:02:09.600 ⇒ 01:02:15.270 Emily Giant: Yep. So like examples we looked at yesterday there were like 36 sales
536 01:02:15.490 ⇒ 01:02:21.030 Emily Giant: and 36 orders went out the door. But there were 8 system mismatches.
537 01:02:21.320 ⇒ 01:02:28.320 Emily Giant: There were also 8 orders that didn’t properly claim. But you’re still getting that 8, which is no.
538 01:02:28.320 ⇒ 01:02:38.019 felipefaria: So weird, how like and how. And I think that this is a problem with netsuite right like of allowing us to make adjustments in a lot
539 01:02:38.910 ⇒ 01:02:41.910 felipefaria: for more quantity than the lot
540 01:02:43.070 ⇒ 01:02:51.950 felipefaria: has. Well, like, I guess it’s an issue of delay of the order being assigned to the lot right? And then adjustments are made before the order comes in.
541 01:02:52.330 ⇒ 01:02:58.850 felipefaria: Yeah, yeah, and that’s and that’s tricky. That’s annoying.
542 01:02:59.200 ⇒ 01:03:00.329 Emily Giant: I’ve gotta hop.
543 01:03:00.330 ⇒ 01:03:02.359 felipefaria: Okay, yeah, let’s talk more about it.
544 01:03:02.470 ⇒ 01:03:10.589 Emily Giant: There is like a food for thought, because it’s 1 that we could probably like talk on for an hour, trying to figure out what to do about it. But I’ll
545 01:03:11.783 ⇒ 01:03:20.139 Emily Giant: and that way we can like, look at very specific examples. But maybe not even tomorrow. I don’t wanna like down
546 01:03:20.560 ⇒ 01:03:25.049 Emily Giant: source to data. And then we can start talking through like.
547 01:03:25.430 ⇒ 01:03:29.119 Emily Giant: because that will help us quantify death fixes and stuff too.
548 01:03:29.120 ⇒ 01:03:30.019 felipefaria: Yeah, yeah.
549 01:03:30.410 ⇒ 01:03:50.059 felipefaria: okay, and we can talk more about it. But it’s it’s an issue of making these updates refreshing faster. Because if we always have accurate view of the lot quantities, then it would prevent us from doing this incorrect adjustments. Like this is how it used to be before right when we were working in
550 01:03:50.260 ⇒ 01:04:09.139 felipefaria: Admin. And I didn’t like. It was essentially impossible to make this sort of issues there, because everything was real time. So we knew exactly how many units we had on that specific shipment, and we couldn’t make more adjustments than what we had available in the log and things like that. So
551 01:04:09.585 ⇒ 01:04:15.080 felipefaria: but okay, I know that you guys have to go. So I’ll talk to you guys later.
552 01:04:15.270 ⇒ 01:04:16.520 Emily Giant: Alright! Thanks.
553 01:04:16.730 ⇒ 01:04:18.669 felipefaria: Alright, thank you. Thank you. Guys. Bye.