Meeting Title: Chuck <> Brainforge - Shipping-Weekly-Meeting Date: 2024-09-26 Meeting participants: Ryan Luke Daque, Nicolas Sucari


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1 00:01:22.240 00:01:23.020 Nicolas Sucari: Hey! Luke!

2 00:01:24.690 00:01:25.930 Ryan Luke Daque: Nicholas.

3 00:01:28.620 00:01:32.720 Nicolas Sucari: I think meeting with him went. Well, I mean, we don’t have

4 00:01:33.550 00:01:34.770 Nicolas Sucari: anything

5 00:01:35.150 00:01:39.000 Nicolas Sucari: really big there. She just needs to go and check

6 00:01:39.020 00:01:45.007 Nicolas Sucari: the data and see what we can do there. We can. We? We need to check just that.

7 00:01:45.720 00:01:48.369 Nicolas Sucari: when when the data is being refreshed. Maybe

8 00:01:48.390 00:01:53.820 Nicolas Sucari: I’m I’m going to try and create like a documentation or

9 00:01:53.990 00:01:56.280 Nicolas Sucari: like adding, How

10 00:01:56.390 00:02:03.949 Nicolas Sucari: yeah, what are the refresh times, or how often did that? Do we get the data to refresh and that kind of stuff, so that we

11 00:02:04.400 00:02:12.660 Nicolas Sucari: we can easily answer those questions like, if we are seeing data that it’s outdated, or we just waiting for a new input, right?

12 00:02:12.780 00:02:16.990 Nicolas Sucari: So I think, having that in a documentation or in a table it should be

13 00:02:17.030 00:02:19.600 Nicolas Sucari: should be good, and we can share that with them.

14 00:02:20.340 00:02:21.920 Ryan Luke Daque: Yeah, makes sense.

15 00:02:23.250 00:02:23.990 Ryan Luke Daque: Yeah.

16 00:02:24.270 00:02:26.270 Nicolas Sucari: And apart from that, I think everything is.

17 00:02:26.360 00:02:27.960 Nicolas Sucari: It’s it’s okay.

18 00:02:28.210 00:02:29.967 Ryan Luke Daque: Yeah. So far, it looks like

19 00:02:30.680 00:02:32.530 Ryan Luke Daque: she was happy with the

20 00:02:32.880 00:02:34.210 Ryan Luke Daque: that changes.

21 00:02:38.750 00:02:41.419 Nicolas Sucari: Let me ask Chuck if he’s joining

22 00:03:07.220 00:03:10.850 Nicolas Sucari: and that skew list analysis is looking good.

23 00:03:12.320 00:03:21.840 Nicolas Sucari: I think what he, what what Dan is looking for is a way to understand which skill is missing in each platform, right? Like trying to have

24 00:03:22.230 00:03:23.530 Nicolas Sucari: the same

25 00:03:23.730 00:03:36.990 Nicolas Sucari: list of skews in all of the platforms, so we can match everything right now. What we will need to do is kind of try to match all of those lists from different sources, and see which ones are missing. In which platform, right.

26 00:03:37.740 00:03:40.150 Ryan Luke Daque: Yeah, well, maybe we can.

27 00:03:41.340 00:03:45.199 Ryan Luke Daque: I think we can make a matrix or or some sort

28 00:03:45.500 00:03:47.440 Ryan Luke Daque: just based on the skew.

29 00:03:47.790 00:03:49.059 Ryan Luke Daque: like we list all.

30 00:03:49.060 00:03:49.380 Nicolas Sucari: Thank you.

31 00:03:49.380 00:03:50.529 Ryan Luke Daque: And then like.

32 00:03:50.530 00:03:51.370 Nicolas Sucari: Yeah, exactly.

33 00:03:51.370 00:03:52.030 Ryan Luke Daque: Yeah.

34 00:03:52.900 00:04:09.530 Nicolas Sucari: I think the easiest way to do it is having the the list that you already created. We just need to normalize the skew column, so that we have always like kind of the same ids there, like what happened with units. And I think there is also

35 00:04:09.910 00:04:12.350 Nicolas Sucari: one that is kind of weird.

36 00:04:13.420 00:04:15.540 Nicolas Sucari: Let me check. Wait.

37 00:04:18.899 00:04:25.459 Ryan Luke Daque: In, I think, in Amazon, or was it shopify? I noticed something weird as well.

38 00:04:26.040 00:04:26.730 Nicolas Sucari: The

39 00:04:27.810 00:04:28.690 Nicolas Sucari: So.

40 00:04:29.650 00:04:31.260 Nicolas Sucari: yeah. And I’m.

41 00:04:31.260 00:04:31.670 Ryan Luke Daque: And.

42 00:04:31.670 00:04:37.580 Nicolas Sucari: They have in Amazon. I think it’s almost the same. Maybe it has some spaces. We need to normalize that.

43 00:04:37.620 00:04:39.460 Nicolas Sucari: Or maybe we can use

44 00:04:40.730 00:04:41.460 Nicolas Sucari: no, yeah.

45 00:04:41.460 00:04:46.400 Ryan Luke Daque: It’s like they have the same skew. But the product id is different, and

46 00:04:46.750 00:04:52.920 Ryan Luke Daque: like the product name, even one is like 10,000 gallons, and the second one is

47 00:04:53.060 00:04:55.340 Ryan Luke Daque: 20,000 gallons, which is.

48 00:04:55.340 00:04:56.150 Nicolas Sucari: Oh no!

49 00:04:56.630 00:05:00.900 Ryan Luke Daque: Yeah, wait. They’re actually different. The skewers.

50 00:05:00.900 00:05:05.619 Nicolas Sucari: No, in the the ship. The ship station one is the one that is kind of weird. I think

51 00:05:05.650 00:05:10.939 Nicolas Sucari: there is. There are some of ship stations that are kind of weird. Yeah.

52 00:05:10.940 00:05:11.580 Ryan Luke Daque: Hmm.

53 00:05:12.460 00:05:13.470 Ryan Luke Daque: okay.

54 00:05:19.560 00:05:23.620 Nicolas Sucari: you’re still! You’re deleting directly in the spreadsheet right.

55 00:05:23.620 00:05:24.420 Ryan Luke Daque: Yeah.

56 00:05:25.330 00:05:25.930 Nicolas Sucari: Okay.

57 00:05:29.250 00:05:29.970 Nicolas Sucari: Okay.

58 00:05:31.780 00:05:33.160 Nicolas Sucari: yeah, because I think.

59 00:05:35.620 00:05:40.829 Nicolas Sucari: yeah, the units once, I’m positive that the ones that we have in product id, those are skews.

60 00:05:41.100 00:05:42.310 Nicolas Sucari: That’s fine.

61 00:05:43.330 00:05:50.879 Nicolas Sucari: And then for ship station we need to check. There are some lines that are kind of the one that says product, id

62 00:05:51.520 00:05:53.130 Nicolas Sucari: kind of strange.

63 00:05:54.560 00:05:57.309 Nicolas Sucari: But we have the skew in that kind of.

64 00:05:57.760 00:05:58.880 Ryan Luke Daque: I don’t know.

65 00:06:01.340 00:06:04.749 Nicolas Sucari: Yeah, we have the skew in there. So we need to distract that. I think.

66 00:06:06.180 00:06:07.790 Ryan Luke Daque: Which one the ship station.

67 00:06:08.430 00:06:13.500 Nicolas Sucari: If if you go to ship station, go to line I think is

68 00:06:14.020 00:06:17.090 Nicolas Sucari: 700. Yeah, yeah, 7, 7, 8.

69 00:06:17.090 00:06:19.310 Ryan Luke Daque: Oh, yeah, it doesn’t have a skew.

70 00:06:20.150 00:06:21.190 Nicolas Sucari: No, no! But.

71 00:06:21.930 00:06:22.530 Ryan Luke Daque: Oh!

72 00:06:22.530 00:06:25.240 Nicolas Sucari: It’s no, it has something.

73 00:06:25.350 00:06:27.280 Nicolas Sucari: and the skew is in there.

74 00:06:28.090 00:06:30.949 Ryan Luke Daque: The skew is in the product. Id.

75 00:06:32.760 00:06:33.729 Nicolas Sucari: No, no, no.

76 00:06:33.970 00:06:41.830 Nicolas Sucari: you see we have there. We have like I don’t know. We have a product id there, or I don’t know what? Which was the column from Ship station we are taking.

77 00:06:41.850 00:06:45.060 Nicolas Sucari: But in this queue column on the spreadsheet we have, like.

78 00:06:45.060 00:06:45.450 Ryan Luke Daque: That’s true.

79 00:06:45.450 00:06:46.040 Nicolas Sucari: Sing.

80 00:06:46.170 00:06:49.839 Nicolas Sucari: and inside the string we have the products. Queue.

81 00:06:53.390 00:06:53.900 Nicolas Sucari: Is he.

82 00:06:54.467 00:06:56.739 Ryan Luke Daque: Yeah, yeah. That’s weird.

83 00:06:58.000 00:07:04.809 Nicolas Sucari: Yeah, I don’t know. Maybe that’s like a mistake. Once because ship station we are importing it right from

84 00:07:05.480 00:07:06.410 Nicolas Sucari: an email.

85 00:07:06.410 00:07:07.770 Ryan Luke Daque: Yeah.

86 00:07:08.090 00:07:09.190 Ryan Luke Daque: yeah, that’s right.

87 00:07:09.190 00:07:09.840 Nicolas Sucari: Yeah.

88 00:07:10.960 00:07:11.620 Ryan Luke Daque: Yeah, I’ll have to look.

89 00:07:11.620 00:07:12.760 Nicolas Sucari: Or I don’t.

90 00:07:13.280 00:07:19.079 Ryan Luke Daque: The yeah, I think it’s the same as the product name. So it looks like it loaded the

91 00:07:19.300 00:07:19.780 Ryan Luke Daque: product.

92 00:07:19.780 00:07:20.130 Nicolas Sucari: Yeah.

93 00:07:20.130 00:07:21.429 Ryan Luke Daque: And do this queue

94 00:07:21.990 00:07:23.939 Ryan Luke Daque: or something like that. Yeah.

95 00:07:25.470 00:07:27.158 Ryan Luke Daque: yeah, I’ll have to look into that one.

96 00:07:27.990 00:07:29.340 Nicolas Sucari: That’s fine. Okay?

97 00:07:30.150 00:07:33.327 Nicolas Sucari: But that’s the only thing on Chip station. Then

98 00:07:34.710 00:07:38.990 Nicolas Sucari: Unis one, I think, yeah, that’s okay. And we don’t have the weights and

99 00:07:39.390 00:07:49.780 Nicolas Sucari: and hide, and all of those data for all of it. But that’s I think that’s fine. I mean, if we can try to understand if we have the same skews across different sources.

100 00:07:49.890 00:07:51.419 Nicolas Sucari: that would be

101 00:07:51.780 00:07:52.950 Nicolas Sucari: that would be okay.

102 00:07:53.630 00:07:54.360 Nicolas Sucari: Right?

103 00:07:54.840 00:07:55.640 Ryan Luke Daque: Yeah, sure.

104 00:07:56.980 00:07:59.360 Nicolas Sucari: So I I know how to do it. Maybe

105 00:07:59.560 00:08:07.210 Nicolas Sucari: maybe we need to. I can copy all of the skews, for from all of the different sheets of the sources

106 00:08:07.310 00:08:16.989 Nicolas Sucari: and paste there in the skew matrix one, and then use like vlookups or something, to see if we have that skill present in each of the different sheets.

107 00:08:17.810 00:08:20.059 Ryan Luke Daque: Yeah, I think that’s that. Yeah, that’s.

108 00:08:20.060 00:08:22.990 Nicolas Sucari: Like. That’s the easiest way to understand. If we

109 00:08:23.040 00:08:24.150 Nicolas Sucari: have it.

110 00:08:24.150 00:08:25.620 Ryan Luke Daque: Exists or not, right.

111 00:08:25.620 00:08:33.800 Nicolas Sucari: Yeah, if it exists or not exist. Yeah, exactly. And then, if we if we see it, it exists, or well, we will need to check, like

112 00:08:33.850 00:08:44.760 Nicolas Sucari: different ways of writing the same skew, maybe with the space with other space and that kind of stuff. But once we normalize it, we can see, we can easily see it exists or not exists.

113 00:08:45.080 00:08:45.930 Ryan Luke Daque: Right.

114 00:08:46.340 00:08:47.680 Nicolas Sucari: In these sources? Yep.

115 00:08:50.330 00:08:51.080 Nicolas Sucari: okay.

116 00:08:56.200 00:08:57.569 Ryan Luke Daque: Not sure if they.

117 00:10:12.010 00:10:17.835 Nicolas Sucari: So do we have. I’m kim just asked if the Daily Kpis are dashboard is

118 00:10:18.280 00:10:19.960 Nicolas Sucari: updated daily.

119 00:10:20.550 00:10:23.099 Nicolas Sucari: I think, yes, right? Yeah. I mean, we have.

120 00:10:24.150 00:10:29.350 Ryan Luke Daque: But it. It wouldn’t show today’s date because of the filter that we added.

121 00:10:30.116 00:10:31.230 Ryan Luke Daque: where? Yeah.

122 00:10:31.720 00:10:36.220 Ryan Luke Daque: if the date, this is not complete yet it wouldn’t show up.

123 00:10:38.330 00:10:43.329 Nicolas Sucari: Yeah, but it’s not showing like yesterday. I think the latest day that we’re showing is Tuesday.

124 00:10:43.980 00:10:45.090 Ryan Luke Daque: Hmm, hmm.

125 00:10:47.280 00:10:48.100 Ryan Luke Daque: yeah.

126 00:10:48.100 00:10:48.940 Nicolas Sucari: Me

127 00:10:52.080 00:10:55.790 Nicolas Sucari: because we don’t have data for yeah, yesterday yet

128 00:10:59.050 00:11:02.200 Nicolas Sucari: we have data from for Tuesday, but not for

129 00:11:03.410 00:11:04.200 Nicolas Sucari: yeah.

130 00:11:06.540 00:11:09.989 Nicolas Sucari: And something strange is the total profit one

131 00:11:10.400 00:11:12.780 Nicolas Sucari: that metric I think we need to

132 00:11:12.920 00:11:15.119 Nicolas Sucari: to check, because we have like empty.

133 00:11:16.120 00:11:17.939 Nicolas Sucari: like, some days, there are empty.

134 00:11:20.245 00:11:20.650 Nicolas Sucari: Maybe.

135 00:11:21.020 00:11:23.060 Nicolas Sucari: Yeah, they didn’t get behind once.

136 00:11:23.370 00:11:25.009 Nicolas Sucari: and we have sales.

137 00:11:26.570 00:11:28.020 Nicolas Sucari: That’s strange.

138 00:11:29.630 00:11:31.919 Nicolas Sucari: Oh, maybe we got no.

139 00:11:34.080 00:11:35.150 Nicolas Sucari: I don’t know.

140 00:11:57.380 00:12:00.530 Nicolas Sucari: Yeah. In the profit one. We have some like blank

141 00:12:01.100 00:12:02.510 Nicolas Sucari: with no data.

142 00:12:03.430 00:12:04.050 Nicolas Sucari: Okay?

143 00:12:11.130 00:12:13.279 Nicolas Sucari: Okay? But let’s let’s finish

144 00:12:13.420 00:12:17.599 Nicolas Sucari: one thing at a time. Yeah, do you? Wanna yeah.

145 00:12:18.660 00:12:27.469 Nicolas Sucari: Let’s go with the the skew list. Let’s finish that. And then we can take a look like deeper into those metrics. Okay.

146 00:12:27.760 00:12:28.609 Ryan Luke Daque: Yeah, sure.

147 00:12:29.610 00:12:33.450 Nicolas Sucari: I, I think that would work. Yeah, we can focus, finish one and then

148 00:12:33.840 00:12:35.300 Nicolas Sucari: continue with the rest.

149 00:12:35.830 00:12:39.400 Nicolas Sucari: So Chuck is not answering. So maybe we can drop. But

150 00:12:39.540 00:12:40.525 Nicolas Sucari: yeah,

151 00:12:41.910 00:12:43.840 Nicolas Sucari: I don’t know if we have anything else

152 00:12:44.110 00:12:44.630 Nicolas Sucari: for now.

153 00:12:44.630 00:12:49.500 Ryan Luke Daque: Sounds good. Yeah, I’ll continue working on the spreadsheet. I’ll let you know if it’s

154 00:12:50.190 00:12:52.940 Ryan Luke Daque: updated. And yeah, we can go from there.

155 00:12:52.940 00:12:53.720 Nicolas Sucari: Excellent.

156 00:12:53.940 00:13:01.850 Nicolas Sucari: Yeah, we can talk later. And once we finish this, let’s yeah. Maybe I I don’t know if you need to continue working on the chat. Gpt stuff.

157 00:13:02.760 00:13:05.949 Ryan Luke Daque: Yeah, I I did schedule a call later, like.

158 00:13:05.950 00:13:06.620 Nicolas Sucari: Yeah, today.

159 00:13:06.950 00:13:12.890 Ryan Luke Daque: Yeah, we can discuss. What else we can do with that with the team. Yeah.

160 00:13:12.910 00:13:19.239 Ryan Luke Daque: like, if there’s anything, any feedback or anything else we can do with that. Aside from

161 00:13:19.530 00:13:20.390 Ryan Luke Daque: just

162 00:13:20.800 00:13:24.890 Ryan Luke Daque: what is what it’s doing now. So yeah.

163 00:13:25.600 00:13:26.230 Ryan Luke Daque: okay.

164 00:13:26.230 00:13:26.930 Nicolas Sucari: Perfect.

165 00:13:27.300 00:13:28.670 Nicolas Sucari: excellent! Thank you.

166 00:13:28.670 00:13:31.870 Ryan Luke Daque: Sounds good. Thanks, Nick. Have a nice day. Bye, bye.