Meeting Title: Henry-Brad quick sync Date: 2026-01-09 Meeting participants: Henry Zhao, Brad Messersmith
WEBVTT
1 00:04:08.200 ⇒ 00:04:09.610 Brad Messersmith: Hey, can you hear me?
2 00:04:10.760 ⇒ 00:04:12.950 Henry Zhao: Hey, Brad, yes I can. How are you doing?
3 00:04:13.330 ⇒ 00:04:14.730 Brad Messersmith: Hey, hold on one second.
4 00:04:21.579 ⇒ 00:04:23.450 Brad Messersmith: Let me see if… can you hear me now?
5 00:04:23.990 ⇒ 00:04:25.040 Henry Zhao: Yeah, Ken, how are you doing.
6 00:04:25.040 ⇒ 00:04:28.290 Brad Messersmith: Oh, okay, yeah, yeah, that’s better. Hey, how’s it going? Happy New Year.
7 00:04:28.290 ⇒ 00:04:29.749 Henry Zhao: Good, Happy New Year to you, too.
8 00:04:30.760 ⇒ 00:04:31.860 Brad Messersmith: You have a good holiday?
9 00:04:32.040 ⇒ 00:04:33.030 Henry Zhao: Yeah, how about you?
10 00:04:33.790 ⇒ 00:04:39.579 Brad Messersmith: Yeah, really uneventful. These days, I don’t stay up too late. I’m, like, an early morning kind of person.
11 00:04:40.330 ⇒ 00:04:42.040 Henry Zhao: Same, same, same. Just got a…
12 00:04:42.270 ⇒ 00:04:45.309 Henry Zhao: Get some relaxing, which is, I think, the most important thing.
13 00:04:45.640 ⇒ 00:04:47.010 Brad Messersmith: Yeah, exactly.
14 00:04:50.220 ⇒ 00:04:55.159 Brad Messersmith: Cool. So, you wanna go through the… the last changes, or what do you… what do you got?
15 00:04:55.540 ⇒ 00:05:03.909 Henry Zhao: Yeah, I have a few things… I won’t take up too much of your time, it’s pretty quick. The one thing I wanted to talk about is,
16 00:05:04.620 ⇒ 00:05:23.330 Henry Zhao: So… yeah, so any… so, two things, right? One, I wanted to get some… your feedback on, like, is there anything else we need to add to here? And then secondly, Robert asked me to talk to you about this chart in Monday. Yeah. Basically, we want to move away from, like, tracking things in Monday.com, because it’s not…
17 00:05:23.420 ⇒ 00:05:24.930 Henry Zhao: best practices, I would say.
18 00:05:24.930 ⇒ 00:05:28.970 Brad Messersmith: Oh, so… Thank you. I hate it. I was told to do this, so yeah.
19 00:05:29.260 ⇒ 00:05:40.410 Henry Zhao: Yeah, we want to help you say, like, what can we do to help you get this out of Monday.com and put this into either Tableau, or if you prefer a spreadsheet, we can put it into a Google Sheet, and update it regularly for you, whatever you prefer.
20 00:05:40.410 ⇒ 00:05:53.029 Brad Messersmith: Yeah, no, no, I prefer Tableau, for sure. My team’s not used to using Tableau, so we might have to cross that bridge in terms of, like, getting them, you know, viewer licenses and whatnot. I know I asked you about, like.
21 00:05:53.240 ⇒ 00:06:10.050 Brad Messersmith: automating to Slack. I might just do the manual step and have them come to my email and then move them over that way or something for now, but yeah, I mean, basically, these are the KPIs that we’re measuring, so at least the key ones are in these… every one on the left that says ops KPI.
22 00:06:10.360 ⇒ 00:06:11.970 Henry Zhao: And honestly.
23 00:06:12.070 ⇒ 00:06:15.750 Brad Messersmith: throw the COGS one out, because it’s kind of worthless. It’s like…
24 00:06:15.750 ⇒ 00:06:16.289 Henry Zhao: COGS one?
25 00:06:16.290 ⇒ 00:06:21.849 Brad Messersmith: You know, well, yeah, look at that one for a second, and you’ll see what I’m talking about if you click on it.
26 00:06:22.520 ⇒ 00:06:23.440 Brad Messersmith: like…
27 00:06:24.550 ⇒ 00:06:40.580 Brad Messersmith: Yeah, so I get this data, you know, it’s January 9th right now. I don’t have it for December, so it’s not really actionable, it’s too late, right? I know that the actions I’m taking should lead to reduced COGS,
28 00:06:40.750 ⇒ 00:06:49.289 Brad Messersmith: But I have no way of knowing, like, throughout the month, or on… you know… I think we’ve talked a little bit about that. So the COGS one of this is just, like, a lease placeholder, so we’re looking at it.
29 00:06:49.480 ⇒ 00:06:50.300 Henry Zhao: But…
30 00:06:50.370 ⇒ 00:06:56.600 Brad Messersmith: you know, if we’re gonna measure this as a KPI, it needs to happen a little differently than this. Other than this one, though.
31 00:06:57.310 ⇒ 00:07:05.959 Brad Messersmith: the number of Eden orders you’ve already kind of given me in the dashboard that we… you sent me. I was, reporting from Monday Board.
32 00:07:06.110 ⇒ 00:07:17.910 Brad Messersmith: the, Eden orders, and now I’m using the Tableau that you gave me for that one, so… you know, whatever, that one’s kind of already taken care of. But the SLA ones, if you go to, like, the MedOps SLA, or… yeah, either one.
33 00:07:18.750 ⇒ 00:07:23.880 Brad Messersmith: There’s two different routes that we can go down with this, and I think…
34 00:07:25.520 ⇒ 00:07:39.029 Brad Messersmith: one of the things that I’ve been thinking about is changing how we’re measuring these KPIs, because this… this has transpired this way only because it’s the info that we have, and it’s kind of the data that they were able to use and get.
35 00:07:39.360 ⇒ 00:07:47.210 Brad Messersmith: But when you look at, like, MedOps SLA, the way I really would prefer to try and measure this somehow.
36 00:07:47.340 ⇒ 00:07:50.140 Henry Zhao: Is to get to a…
37 00:07:50.350 ⇒ 00:08:05.270 Brad Messersmith: like, percentage of orders that are within the lead time, and on-time delivery more kind of metric, right? So if we have, I think, 72 hours for our medical team, you know, Beluga and everybody to move orders through.
38 00:08:05.850 ⇒ 00:08:11.830 Brad Messersmith: Then, how do we figure out what percentage of orders did not make it in that time frame?
39 00:08:11.990 ⇒ 00:08:24.280 Brad Messersmith: And then start to break those down into categories and, you know, do the troubleshooting kind of stuff from there. But that’s, ultimately, that’s the KPI I wish or I would like to be measuring, is…
40 00:08:24.570 ⇒ 00:08:36.009 Brad Messersmith: you know, not… ideally, it would be, like, a 95% or 99% of orders are happening within 72 hours through the MedOps system, I guess.
41 00:08:36.360 ⇒ 00:08:41.400 Brad Messersmith: And then those 1% are the ones that we… my team has to handle, right?
42 00:08:42.429 ⇒ 00:08:55.480 Henry Zhao: Does that make sense? Yeah, so just to clarify, right now, you’re getting this data from somewhere, and you’re just creating a ticket every week, and putting in the number of pending orders, number of orders last 30 days, and then dividing it to get the percent out of SLA, and then plotting it here, basically, right?
43 00:08:56.120 ⇒ 00:09:10.629 Brad Messersmith: Yeah, and really, I’m taking this from a Word doc that they’re pulling from reports every day, so we could… we could pull together the team, and it’s the same sort of thing I think we’ve done. If you go to the FarmOps SLA,
44 00:09:11.230 ⇒ 00:09:14.459 Brad Messersmith: It’s the same thing we’ve started to do at one point.
45 00:09:15.660 ⇒ 00:09:30.389 Brad Messersmith: in terms of, like, understanding, it’s basically filters, right, from the order export. So, if we can figure out what those filters are and narrow it down to get these numbers to be… even if they’re directional, Henry, it’s still a huge improvement, I think.
46 00:09:30.800 ⇒ 00:09:44.639 Brad Messersmith: You know, if it says 3,425 here, and your data is showing 3,500-something-something, that’s okay. Like, I think there might be some of that, right? But I don’t really know.
47 00:09:44.830 ⇒ 00:09:51.690 Brad Messersmith: Ultimately, though, it really is just filters, so they’re filtering down the data to get to these numbers, which seems doable in town as well.
48 00:09:52.660 ⇒ 00:10:09.489 Brad Messersmith: Yeah, it’s all from… I… well, we’d have to, ask Amy and Katie, maybe, on the MedOps one, but I’m 99.9% sure it’s all coming from the orders export that we showed you. So, if you can get a webhook of that data, which it seems like maybe you have some of.
49 00:10:09.870 ⇒ 00:10:12.650 Brad Messersmith: then we’re pretty much there.
50 00:10:12.650 ⇒ 00:10:15.829 Henry Zhao: the orders from, app.health, right?
51 00:10:16.760 ⇒ 00:10:21.380 Brad Messersmith: Yeah, go, yeah, yeah, I can show you, yep. So Monday, so Monday… Right here, yeah.
52 00:10:21.380 ⇒ 00:10:25.100 Henry Zhao: for the last 30 days. Right, okay, so Monday, they pulled this for the last 30 days.
53 00:10:25.230 ⇒ 00:10:36.550 Henry Zhao: they count how many are… there are total, so, like, this Monday, they pulled in, it was 29,709 tickets that last 30 days, and then 2,13 of them were out of SLA, basically.
54 00:10:37.240 ⇒ 00:10:38.000 Brad Messersmith: Correct.
55 00:10:38.570 ⇒ 00:10:45.979 Henry Zhao: Actually, so it would be actually January… it would be December 6th through January 5th, right? Because you would want to pull 3 business days behind.
56 00:10:46.850 ⇒ 00:11:00.620 Brad Messersmith: Yes, yeah, it goes… and honestly, these are… this is where we probably want to at least, at some point, get Katie and Amy and the team involved, so they can specify the exact details, because I don’t know them that well, but if you go, like, for the farm ops side.
57 00:11:01.160 ⇒ 00:11:11.280 Brad Messersmith: like, for today, because they’re pulling these reports daily. Every single day, it goes into a Word doc. Right now, we’re just turning… putting it into Monday once a week, just because that’s what I have to report DLT on.
58 00:11:11.540 ⇒ 00:11:16.469 Brad Messersmith: But… There’s no reason we wouldn’t do this daily, I don’t think, if it’s all connected.
59 00:11:16.640 ⇒ 00:11:17.149 Henry Zhao: Yeah, if it’s.
60 00:11:17.150 ⇒ 00:11:17.540 Brad Messersmith: It’s connected.
61 00:11:17.540 ⇒ 00:11:20.170 Henry Zhao: then we can have it daily in Tableau, yeah, you’re right.
62 00:11:20.170 ⇒ 00:11:32.199 Brad Messersmith: Yeah, so then I think, like, they’re pulling their report today, starting from the 5th, and then 30… 30 days back, and I actually want to say they’re going all the way back to, like, an August date right now.
63 00:11:33.020 ⇒ 00:11:41.909 Brad Messersmith: So, those are, like, we probably need to have some conversations once you feel ready to talk about how that would work, or what those filters need to be.
64 00:11:42.260 ⇒ 00:11:43.080 Henry Zhao: Okay.
65 00:11:43.930 ⇒ 00:11:59.499 Brad Messersmith: But again, it goes back to the question here, because it’s the same thing for the pharmacy side, where, like, that number of orders, essentially, we’re turning this into a percentage over the last 30 days of orders, or whatever it is. But realistically, I would rather see, like.
66 00:11:59.950 ⇒ 00:12:04.419 Brad Messersmith: A percentage of how many of our orders missed, if that makes sense.
67 00:12:04.650 ⇒ 00:12:14.680 Brad Messersmith: This, like, 30-day number, this 30,000 orders, or 29,000 orders, or whatever it is, is kind of, like, arbitrary. It’s just a number that it’s consistently similar
68 00:12:15.090 ⇒ 00:12:23.050 Brad Messersmith: And it scales with our volume, so it is helpful to have that component, but it’s like a just made-up sort of 30-day number versus…
69 00:12:23.230 ⇒ 00:12:28.650 Brad Messersmith: If we were actually measuring as a percentage of the orders that came through, it would be a different percentage, I think.
70 00:12:28.650 ⇒ 00:12:31.049 Henry Zhao: Yeah, definitely. It definitely would be, right? So…
71 00:12:31.220 ⇒ 00:12:33.430 Brad Messersmith: Yeah. So that’s kind of, I mean…
72 00:12:34.300 ⇒ 00:12:43.979 Brad Messersmith: and it might be, like, a step in between, because we have this pattern, and it’s working really well, and it’s really well established, so I also don’t want to, like, drop a grenade in the middle of things.
73 00:12:44.130 ⇒ 00:12:47.809 Brad Messersmith: you know, by moving to Tableau, and then also changing…
74 00:12:48.310 ⇒ 00:12:54.789 Brad Messersmith: about how we calculate it, so I want to be careful, too, on the, like, user side, that we’re not disrupting too much, either.
75 00:12:55.180 ⇒ 00:13:06.049 Henry Zhao: Yeah, we’ll figure out a way to kind of transition smoothly, but when we do look at it that way, the number’s gonna look worse, just to give you a heads up, right? Because here, you’re, like, kind of artificially…
76 00:13:06.300 ⇒ 00:13:13.429 Henry Zhao: decreasing the percent of SLA, because you’re looking at 30 days, and how many are out of SLA at that moment, right? So…
77 00:13:13.660 ⇒ 00:13:17.469 Henry Zhao: I’m not out of my side, but how many were… are still open at that moment?
78 00:13:18.420 ⇒ 00:13:21.480 Brad Messersmith: Right, right, yeah, but if you look at, like.
79 00:13:21.910 ⇒ 00:13:25.220 Brad Messersmith: Let me think through this, because how would we calculate it? If we had…
80 00:13:27.090 ⇒ 00:13:31.020 Brad Messersmith: like today, we have 213 out of SLA, let’s say.
81 00:13:32.350 ⇒ 00:13:42.169 Brad Messersmith: How do we know that number of orders, 213 over what denominator, then? If it’s not 30 days? You know what I mean? Like, there has to be…
82 00:13:42.280 ⇒ 00:13:48.989 Brad Messersmith: some measure of the number of orders through the timeframe, but I’m not really sure how to establish that. We need to think a little bit more about that.
83 00:13:48.990 ⇒ 00:13:54.750 Henry Zhao: Let me look through the data, and I’ll give you a suggestion, and then you can let me know if that sounds good to you.
84 00:13:55.180 ⇒ 00:14:04.229 Brad Messersmith: Okay, because there are, in the data, I think there are dates and timestamps and stuff, so it could even be that, like, we say there’s a 48-hour rule, and anything that’s a yes or a no.
85 00:14:04.530 ⇒ 00:14:05.560 Henry Zhao: Yeah, exactly.
86 00:14:05.560 ⇒ 00:14:06.490 Brad Messersmith: And then that’s the…
87 00:14:06.490 ⇒ 00:14:16.739 Henry Zhao: past three days, you just say, did it get it shipped in 3 business days or not? So just each day, you would have how many percentage actually ended up getting out with an SOA.
88 00:14:17.360 ⇒ 00:14:19.059 Brad Messersmith: Yeah, right, precisely.
89 00:14:19.060 ⇒ 00:14:19.650 Henry Zhao: Yeah.
90 00:14:20.490 ⇒ 00:14:26.909 Brad Messersmith: But for now, I think you can think about what it would take to at least put this into Tableau, because that would be a big…
91 00:14:27.180 ⇒ 00:14:28.150 Brad Messersmith: Big help.
92 00:14:28.900 ⇒ 00:14:43.700 Henry Zhao: Yeah, we already have it, but it’s not gonna match, so I just wanna next look at why those numbers don’t match, but we might be moving away from BASC soon, in the next few weeks, so if we do that, then, we might have a different, like, data set to see maybe if that’s more accurate.
93 00:14:43.860 ⇒ 00:14:50.950 Henry Zhao: But before we talk about that, is there any update from Gal on bio size, or any of the other things we talked about last month when we met with Gal from BASC?
94 00:14:51.620 ⇒ 00:15:03.210 Brad Messersmith: Not really, no, not from my side. I mean, I’ve been pounding him on other, kind of, more important… it’s super important to me, so I don’t want to state it like it’s not important, because it’s definitely very high on my list, but…
95 00:15:03.370 ⇒ 00:15:14.920 Brad Messersmith: I’m trying to get him to connect and integrate new pharmacies I’m trying to bring on, and I’ve got meetings with them, asking, hey, what’s the timeframe and stuff, so yeah, I mean, it’s just a bottleneck across the organization, is what it is.
96 00:15:16.060 ⇒ 00:15:33.459 Henry Zhao: And another thing is, when I pull refills data, so sometimes I want to see how many refills are left, and pull the people that have no more refills, and I’m seeing some people with negative 1 refills. Do you know, from talking to Basque, if they’ve ever mentioned anything like that, and why there might be orders with negative 1 refills?
97 00:15:35.090 ⇒ 00:15:38.010 Brad Messersmith: Sorry, say that one more time, you’re seeing negative refill…
98 00:15:38.010 ⇒ 00:15:41.569 Henry Zhao: Yeah, so some people will have, like, negative 1 refills.
99 00:15:44.440 ⇒ 00:15:46.659 Brad Messersmith: Yeah, no, I am… I’m not sure…
100 00:15:47.050 ⇒ 00:15:47.770 Henry Zhao: I do remember.
101 00:15:47.770 ⇒ 00:15:52.710 Brad Messersmith: I remember vaguely seeing a message about it in Slack, but I… I don’t know much about that.
102 00:15:52.840 ⇒ 00:15:53.580 Henry Zhao: Okay.
103 00:15:53.900 ⇒ 00:16:01.130 Henry Zhao: Alright, other than that, was there anything else that you wanted to add to this dash in the meantime, that we haven’t talked about?
104 00:16:02.560 ⇒ 00:16:19.490 Brad Messersmith: No, I don’t think so. I did schedule a Monday report from this, because I think in some cases I can kind of optimize this… the views on this to give me automatic reports now. Can you change the dates, though, to 2026 and see…
105 00:16:19.700 ⇒ 00:16:24.589 Brad Messersmith: For a while there, it looked like it wasn’t quite catching all the info, but it might…
106 00:16:24.750 ⇒ 00:16:26.580 Brad Messersmith: I didn’t look at it today, or…
107 00:16:26.900 ⇒ 00:16:28.659 Henry Zhao: Does that seem right?
108 00:16:29.960 ⇒ 00:16:30.770 Henry Zhao: Let’s see…
109 00:16:32.670 ⇒ 00:16:37.760 Brad Messersmith: It was really the, January, like, yeah, go by week or day. It looked like it was…
110 00:16:41.560 ⇒ 00:16:48.590 Brad Messersmith: it kind of looked like there was blank spaces from the first to the fifth when I was pulling this up, or the numbers looked super low. You see what I’m talking about?
111 00:16:48.740 ⇒ 00:16:49.810 Henry Zhao: Yeah, is that…
112 00:16:49.810 ⇒ 00:16:59.969 Brad Messersmith: 800, 300, 400 days, and then, like, zero coming in. It seems incorrect to me. What I’m hearing from the marketing team is that our order volumes are going up.
113 00:17:00.070 ⇒ 00:17:01.429 Brad Messersmith: Especially new orders.
114 00:17:01.850 ⇒ 00:17:11.179 Brad Messersmith: it’s not exactly what’s reflected here, but also, this is kind of like filling, you know, it’s not like an entire month, you know what I mean? So I understand it’ll look lower.
115 00:17:11.690 ⇒ 00:17:16.070 Henry Zhao: So maybe it wasn’t… there weren’t many shipments during the, like, holiday break.
116 00:17:18.819 ⇒ 00:17:23.159 Brad Messersmith: Yeah, these are just shipped orders, right? Yeah, that does make sense.
117 00:17:23.269 ⇒ 00:17:29.049 Brad Messersmith: Yeah, that would make sense, right? Because most of the holiday schedule, they were down those days, right?
118 00:17:29.190 ⇒ 00:17:29.969 Henry Zhao: Yeah.
119 00:17:30.290 ⇒ 00:17:33.279 Brad Messersmith: So, yeah, okay. Yeah, I’m following, that does make sense.
120 00:17:33.280 ⇒ 00:17:46.670 Henry Zhao: Okay, so then, when you said there were discrepancies, it could also be the date also. Like, this is the ship date, and maybe a report you looked at maybe had, order date, or sent to pharmacy date, so there might be a little bit of discrepancy there, but directionally, hopefully it’s… it’s correct.
121 00:17:46.670 ⇒ 00:17:56.540 Brad Messersmith: Yeah, I wasn’t even looking at another report, it just looked low to me. Like, you know, just gut feeling looked low, but that does make sense when you break it down by day. I was looking at it by week.
122 00:17:56.840 ⇒ 00:18:00.479 Brad Messersmith: So yeah, that… I think that logically kind of checks out now.
123 00:18:00.670 ⇒ 00:18:14.669 Brad Messersmith: Yeah, that’s the only… this has been useful. I mean, I’ve been using this quite a bit instead of my manual spreadsheet, so it definitely has the info, and I’m kind of getting used to which views are most useful, and kind of thinking about how to export that and get the team using it.
124 00:18:14.670 ⇒ 00:18:21.140 Brad Messersmith: As far as setting up people with viewer licenses, how… is that free? How difficult is that? Like, what does that require?
125 00:18:21.360 ⇒ 00:18:27.210 Henry Zhao: It’s not free, but between you and me, kind of what we do at Brainforge is we just all have a shared login, so you can feel free to do that.
126 00:18:28.140 ⇒ 00:18:28.690 Brad Messersmith: Okay.
127 00:18:28.690 ⇒ 00:18:29.120 Henry Zhao: Can I?
128 00:18:29.120 ⇒ 00:18:30.680 Brad Messersmith: Can I set that up then on my side.
129 00:18:31.450 ⇒ 00:18:38.369 Henry Zhao: Yeah, or you can just give them your login through a 1Pass or something. Like, I’m using Robert’s, but we have this, like, Eden at Brainforge for all of Eden.
130 00:18:39.540 ⇒ 00:18:41.080 Brad Messersmith: Or just the viewer license.
131 00:18:41.080 ⇒ 00:18:42.029 Henry Zhao: Yeah, exactly.
132 00:18:42.470 ⇒ 00:18:55.800 Brad Messersmith: Yeah, okay. Alright, yeah, that makes sense. Alright, for now, I’m gonna, like, start sending some stuff out manually and get people’s feedback and see what they think about the reports, but, you know, it’s definitely a good start. I think it’s working pretty well. It gives me the basic kind of info I need, for sure.
133 00:18:56.160 ⇒ 00:18:59.320 Henry Zhao: Cool, and if you need me to add anything else, like, something comes up, just let me know.
134 00:18:59.720 ⇒ 00:19:01.409 Henry Zhao: And we will get that added.
135 00:19:02.040 ⇒ 00:19:05.230 Brad Messersmith: Yeah, okay, that sounds good. I’ll keep pinging you if things come up.
136 00:19:05.420 ⇒ 00:19:06.640 Henry Zhao: Alright, thanks, Brad.
137 00:19:06.840 ⇒ 00:19:09.360 Henry Zhao: Awesome. Thanks, Henry. Appreciate it. Take care. Bye.