Tips For Aligning the Forecasting Process between Finance and Operations with Amber Johnson
In this episode of Financial Modeler’s Corner, host Paul Barnhurst sits down with Amber Johnson to discuss forecasting, financial modeling, and how operational decisions impact financial outcomes. They explore the connection between logistics forecasting and financial forecasting, the importance of tracking forecast accuracy and bias, and how small operational issues can create large financial impacts. Amber also shares lessons from her experience working with data, forecasting demand, and helping businesses improve their systems and decision-making.
Amber Johnson is a fractional industrial engineer and the founder of Peachy Profitability, where she helps teams work smarter through process improvement, data storytelling, and automation. She began her career as a “beer psychic,” forecasting demand at Anheuser-Busch, and has since built logistics networks, optimized warehouse flows, and guided businesses through transformational change. Amber is also the creator of Office Hours with Amber, a weekly livestream that encourages continuous improvement with curiosity and confidence.
Expect to Learn
How logistics forecasting connects to financial forecasting
Why forecast accuracy and bias matter in decision-making
How operational drivers influence financial results
Ways finance, sales, and logistics teams can align better
How Excel and data analysis support forecasting and planning
Here are a few quotes from the episode:
“Sometimes the smallest operational issue becomes the biggest financial problem later.” – Amber Johnson
“Good forecasting isn’t about being perfect, it’s about learning and adjusting.” – Amber Johnson
Amber Johnson shared practical insights on forecasting, operational drivers, and financial modeling. She highlighted how understanding logistics and operational data can improve financial decisions and help businesses plan more effectively.
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In today’s episode:
[02:23] – Amber’s Background
[08:21] – Founding Peachy Profitability
[10:38] – Learning Excel and Forecasting
[15:37] – Logistics vs Financial Forecasting
[18:59] – Forecast Accuracy and Bias
[23:59] – Finance vs Logistics Perspectives
[31:41] – Aligning Teams Around Goals
[36:07] – Favourite Excel Shortcuts
[37:46] – Rapid Fire: modeling Opinions
[44:39] – Where to Find Amber
Full Show Transcript:
Host: Paul Barnhurst (00:00):
Financial Modeler's Corner is the world's Premier modeling podcast. It is brought to you by Financial modeling Institute, the world's leading financial modeling accreditation organisation. Welcome to Financial Modeler’s Corner. I am your host, Paul Barnhurst, A k the FP&A guy. And this is a podcast where we talk all about the art and science of financial modeling with distinguished modelers from around the globe. The Financial modelers Corner podcast is brought to you by the Financial modeling Institute. FMI offers the most respected accreditations and financial modeling, and that's why I earned my advanced financial modeler this week. I'm thrilled to welcome onto the show. Peachy, also known as Amber Johnson. How you doing, Amber? Oh, peachy. Keen jelly
Guest: Amber Johnson (00:50):
Bean. Excited to be here.
Host: Paul Barnhurst (00:51):
Good answer With the peachy. We have to start. Before I re ad your background, you got to let everybody know what your sweater is. They can't quite see it.
Guest: Amber Johnson (00:58):
Yes. Okay. The infamous EBITDA sweatshirt, we'll
Host: Paul Barnhurst (01:01):
Talk more about it later, but let's get started with it.
Guest: Amber Johnson (01:03):
Earnings
Host: Paul Barnhurst (01:04):
Before interest therapy, depression and anxiety for those who are not watching the video. All right. So I just had to share that. We'll do a tease here in a minute. You'll get to learn why she's wearing that sweater. But before we get there, we got a couple questions. So a little bit about her background. Amber is a fractional industrial engineer and the founder of Peachy Profitability, where she helps teams work smarter through process improvement, data storytelling, and automation. She began her career as a beer psychic, forecasting demand at Anheuser-Busch, and has since built logistics networks, optimized warehouse flows, and guided businesses through transformational change. Amber’s superpower is making complexity approachable. She’s the creator of Office Hours with Amber, a weekly livestream series that empowers learners to embrace continuous improvement with curiosity and confidence. Known for her warm, energizing presence.
(02:04):
Amber is someone who always sees the sparkle in each person brings and helps them see it too. She believes great systems are human-centered and the best growth starts with, I don't know yet, but I know I will. I love the, I don't know yet. That is one of my favourite frameworks. My daughter now, because I taught it to her where I said the other day, I'm not a good cook. She goes, you're not a good cook yet, dad. And then she lectured me and I'm like, thank you. So love that framework. Before we get into the main part of this, we got to start with the question. Ask everybody tell me that horror story, worst model you ever had to deal with.
Guest: Amber Johnson (02:42):
The worst model that I ever had to deal with was I was wearing this sweater when I was creating my first financial model, and I think I was making this financial model before the 2024 act of cell conference where I got to meet you and chitchat a bit more and then watch Ian sch snow's presentation on all of the financial modeling tips, tricks, things that people hide, all of the hidden cells and all that. And I was doing every single one of those little things that he said was horrific. And so the nice thing is in the middle of me creating one of the worst models, I actually created the second to worst model, which was for one of my clients, our five-year projections of what we think our business could do. And of course at that time we were operating out of the, maybe that was a 2023 conference probably we were operating out of my friend's attic. And so I had to make a financial model to be like, I promise, and I don't use the P word. I was like, I project that we are going to do some pretty cool things here shortly. And somehow that Excel model with all of the things I learned at the X Excel conference ended up getting us some seed money. But I look back at it now and I'm deeply embarrassed and it shall never see the light of day by anyone but me.
Host: Paul Barnhurst (03:59):
Come on, you're not going to send it over to me so we can post it with this episode.
Guest: Amber Johnson (04:04):
0% chance. Yeah, no chance. So bad. I looked back and I was like, okay. And the nice thing I heard at that conference also, well you guys have sang all the time, is all models are wrong, some are useful. And so that thing was very wrong, but it was useful enough to get us in the door. So God bless,
Host: Paul Barnhurst (04:23):
There's lots of garbage models out there. And I say that in a nice way. I mean there's a lot of challenges with them, whether it's design in some cases, but there may be some decent assumptions and they may drive the business, which I would prefer. Or there's ones that, hey, they may be great design, but the assumptions are rubbish and everything in between. We tend to think if they're well-designed, they're a better model. We tend to trust the modeler more. And generally that's true. There's usually a good reason. But there can be polished rubbish as well.
Guest: Amber Johnson (04:54):
Oh yeah. I think it was the shock factor of how many details were taken into consideration. So engineer brain, I'm like, okay, if I just put all of the variables all there, you can't question anything, which is true because they didn't look too two too close. I also operate off of a very pessimistic view, so thankfully I was already giving most times working with salespeople, you get the optimistic it's going to be great, there's nothing that could possibly go wrong. And then I'm the other end of the spectrum where I'm like, I don't believe any of this. So thankfully my pessimism mixed with the design somehow worked in our favour.
Host: Paul Barnhurst (05:28):
Hey, take what you can get. It fits with the shirt you're wearing there. And so our audience knows, the first time I met Amber was at the financial modeling world championship, the conference that they have, and she was wearing that shirt. I think she was in the row right in front of me, if I remember correctly or was, no, you might've been behind me. I think you were a row behind me actually. But either way, I remember looking and seeing that shirt and it started a conversation because who doesn't want therapy, depression and anxiety when building models?
Guest: Amber Johnson (06:00):
An added beat, a feature of this is since I wore so many late nights, what you can't see is there's quite a few grease stains from whatever food I was eating. So this has become, I think you can see a little one right there. So this is truly blood, sweat, tears and Greece from that whole experience. So
Host: Paul Barnhurst (06:17):
Well, there you go, that first model, but you got some seed money, so that's always a good thing. And you've improved your modeling. I don't think many of us would want to show our first model. You're not alone on that. I've built some, what I like to call franking models over the years. There are hideous, some more recent than I probably want to admit, but we won't go there. See, that's why I talk about it instead of build them. And everybody thinks I know what I'm doing. I let the experts sound smart and then they think I'm smart. See, I got it down
Guest: Amber Johnson (06:47):
Exactly. See I Franken model as well where I take little templates of little things that I've seen along the way, but then I try to put 'em all together and then they're all kind of icky and bad, but we're getting better continuous improvement. Speaking of using apparel as a way to get people to talk to you, the most of my networking has come from these boots, the sparkly boots. If you're a man, woman, whoever and want to get people to talk to you without having to do much, these do more networking than I have ever.
Host: Paul Barnhurst (07:20):
Well, I have a pair of sparkly boots and I wore 'em in Vegas and nobody even looked at me twice.
Guest: Amber Johnson (07:25):
What? Wait, did you wear it to the conference or did you wear it to the finals?
Host: Paul Barnhurst (07:30):
I think it was the finals. I wore it.
Guest: Amber Johnson (07:32):
Well, everyone was kind of dressed up for the finals. That's fair. And it was dark in there.
Host: Paul Barnhurst (07:36):
I mean I had a sparkle vest too. It kind of stood out. Lots of people at the conference commented, nobody at the hotel commented I was a vase.
Guest: Amber Johnson (07:45):
Oh yeah, yeah. The hotel all just kind of like, oh, I've seen that. Okay. Yeah, Vegas in December, the rodeo, there's way crazier stuff happening than us,
Host: Paul Barnhurst (07:58):
But I tried to stand out with my sparkles, so
Guest: Amber Johnson (08:01):
I think you did.
Host: Paul Barnhurst (08:01):
But I need to use it for more to lead gen, so I'll start wearing it more often.
Guest: Amber Johnson (08:05):
Good. Lemme know how it goes.
Host: Paul Barnhurst (08:08):
In fact, before we're done before rapid fire, I might throw it on for those who are watching video just to see what kind of responses I get. We shall see. You never know. Perfect. Alright, so you run your own business called Peachy Profitability.
Guest: Amber Johnson (08:21):
Yes. First,
Host: Paul Barnhurst (08:22):
Tell us what the business does and second, how did you come up with that
Guest: Amber Johnson (08:25):
Name? Wonderful question. So in short, I help small and medium sized businesses do more with less, make work, work better, work smarter, not harder. Essentially, the easiest way to understand if I could be of use is if you say in the last week, if you've said, I wish I had time to do blank, or there's got to be a better way to do blank, that is like Amber territory. I offer industrial engineering services, but to the normal person, it's just continuous improvement process systems, engineering, making stuff work better and peachy profitability. Where that name came from is, I mean, truly peachy has been a part of my vocabulary for the last decade. So when I worked in corporate, a lot of times people would be like, oh hey, how are you doing? Oh, I'm good. And I was just so bored with that. I was like, corporate is going to be as miserable as you make it.
(09:18):
So how about I said peachy king, jellybean, add a little pizazz to it. The day is only so long. So peachy had already been a part of my vocabulary even on the least of peachy days. But as a business, we've got profitability, which is a very common success metric for businesses. And that kind of symbolises the systems, the technical, the mechanics of how successful businesses are ran. Peachy is the warm fuzzy human side of who runs successful businesses. And I have observed through much of my career that if you focus on one without the other, usually the root cause of what fails is the other that you weren't focusing on. And so what I do is I try to create human-centered design solutions to push businesses forward.
Host: Paul Barnhurst (10:06):
You have to think about people when you design things.
Guest: Amber Johnson (10:09):
I know. Gross, right?
Host: Paul Barnhurst (10:12):
I thought with models and all that you just built for the computer these days.
Guest: Amber Johnson (10:17):
I know. And it's crazy to think that behind every dollar spent, there's someone running a card. Decisions made by people.
Host: Paul Barnhurst (10:25):
Horrific. Mind you, I need to go run a card. Thanks. You mentioned your background is in industrial engineering. So what led to your love of Microsoft Excel and modeling? How'd that come about?
Guest: Amber Johnson (10:38):
Great question. So I have a professor from my alma mater, western Michigan University, Dr. Bob White, and he is infamous. And so in his classes you have engineering economy and operational design and all of these different classes where you learn how to do every single crazy thing in Excel. And what he believed, and he is so far ahead of his time, he was like, every company you go to work at is going to have Excel. So why would I teach you how to do crazy forecasting and modeling in all these other programmes if you might not have licenced access to these huge programmes? So I learned how to do 12 versions of forecasting with halt winter and multiple regression, linear regression, all of these different things by hand in Excel back in 20 14, 15, 16, 17, 18. Took an extra lap there, but I would say it started in college and then when I got to my professional career, it only continued.
(11:37):
And what I realised is moving between legacy systems like SAP and you've got VP and you've got this and you've got that and you've got data that lives all over the place and Snowflake all these different places, but all have an export to Excel button. And all of them are reporting similar data but not quite the same. So what I learned early when I was interacting with a lot of sales directors and they're trying to pitch all these numbers at me and I'm like, I don't think that's the real data, but their system says, so I would pull all of the databases all together in Excel and compare them and be able to quick draw data in Excel quickly used to be access to Excel, but power query, God bless, we're here. But anyways, learning how to quick draw data so that they didn't hold bad data against me and I could outwit them with the data. There's a long way to say it's just followed me the entire time. As soon as I think I'm done with Excel, she comes right back. And it's just the easiest way to manipulate data and wield it to tell the story you need to to make the decision you need to make.
Host: Paul Barnhurst (12:38):
Now I did notice you called it a she, when did it become a she?
Guest: Amber Johnson (12:42):
A she. Oh, oh yeah, Excel. She's too smart to be a man. She knows too much. Too much. Maybe there's some days that she takes turns, but yeah, she's too smart. She's got too much.
Host: Paul Barnhurst (12:53):
Got it. Alright, we'll go with that. I don't have a dog in this fight, so I don't care.
Guest: Amber Johnson (12:57):
And what about modeling? So I would say that I'm actually a phoney modeler. I would say I am like a by proxy
Host: Paul Barnhurst (13:06):
Have to hang up now.
Guest: Amber Johnson (13:07):
I know the fact that I even got the invite is quite shocking. I would say with the beginning of in school we learned a lot of modeling and we did financial statements that we do some of that in some of our classes. But really the first financial model or real big model that I created was from 2018 and then 20, 24 many years later. And I would say in, but the aspects of a good modeler. So being able to understand scenarios and variables and having periods of time that you're looking at in the future and the past and being able to kind of live in the future and the past all at the same time. That skill was built at Anheuser-Busch while I was doing sales forecasting because being able to say from week five weeks out and three weeks out, my forecast accuracy improved by this much and that impacted sales in these ways, which impacts financials.
(14:02):
I would say my understanding of the variables that impact modeling were built at Anheuser-Busch. But yeah, I would not say I'm a modeler. And the funny thing about how that comes up in my current work is there's a lot of people that were requests like, oh, you made a financial model that got you money, can you make one for me? And I'm like, I know too many fantastic financial modelers that I feel like a phoney every time. So I had to print off this thing. It's over there. It says I might not be the best, but I'm the best they have access to, so I'm going to do my best. And that's how I approach every single financial model is I'm going to help you get through a lot of variables, but when you got the money, go to these big dogs,
Host: Paul Barnhurst (14:42):
Go talk to somebody else who's done it for 30 years or that does it for a full-time living.
Guest: Amber Johnson (14:47):
Exactly. In engineering there's a code of ethics of if you're not an electrical engineer, don't touch the electrical box. And so similarly as I follow the same code of ethics in financial modeling is if you're not an accountant, don't pretend you are similarly. So I always go in with the idea of I will help you managerially think through these variables, but pass that it's with you and God and your bookkeeper.
Host: Paul Barnhurst (15:12):
See, my dad was an electrician, so I just play with the electrical.
Guest: Amber Johnson (15:14):
I'm too scared of it
Host: Paul Barnhurst (15:15):
And I don't have an accounting degree and I still play with the models. So I guess that tells you a lot. You know what? God bless. So I know you focus a lot on direct to consumer businesses and logistics forecasting. So maybe talk a little bit about the differences between building a logistics forecast and a financial forecast or similarities. You can go either or both.
Guest: Amber Johnson (15:37):
Yeah, yeah, for sure. I would say every version of forecasting you could possibly do really depends on what your specific goals are. So if you're having, let's say there's a scenario where your lead times with your suppliers is like three days, you have infinite availability to supply your production forecast isn't going to matter that much If you have so much access that production is not that big of an issue. Why have a production forecast? But if your cashflow is tight, you need to be forecasting your finances specifically. Now the flip side, normally especially in American manufacturing, there are crazy lead times that have a lot of variability and have a lot of seasonality to them. And so that's where by getting, and when I say logistics forecasting kind of getting more for my client. Good Trade depot, they do beverage co manufacturing for small businesses.
(16:30):
And so when you're procuring can labelled tray 16 ingredients all across the country and some of those things can freeze or some of those things come in frozen or if the weather is a certain way, it could freeze on the truck and explode. There's so many logistical constraints inside of food and beverage that production forecasting and forecasting all of your components becomes really specifically important. And because of that, our cash flow is based on deposits in and completed production. So if we have all of these delays in variability happening with the components coming in, what's going to happen to your cashflow if you're not completely certain on when the production's going to happen? So it's kind of like I see financial forecasting is good to be able to make bigger decisions based on reinvestment of funds, et cetera, but it's more of a lagging indicator in my world because I know my leading indicators are like if I have one thing that has a late A TA that's going to tell my finances six weeks out from now that they're going to be a little messed up. So leading, lagging in my world,
Host: Paul Barnhurst (17:37):
Sure. I mean finance in general is a lagging indicator. It totally makes sense. Logistical is going to have more of your leading indicators that ultimately if you understand those indicators, you can see how they're going to impact the financials downstream, whether it be production delays or getting something early or whatever it might be. But that makes a lot of sense. I always like to say, yes, financials are important, but you really need to understand the operational drivers. And next question I kind of want to ask, when you and I chatted, we had a chance to chat before this recording, kind of talk through a little bit of things. You mentioned how little problems matter in logistical forecasting a lot, how they can create some real challenges. Can you elaborate on that?
While my background is in fact, I am also passionate about financial modeling. Like many financial modelers, I was self-taught. Then I discovered the Financial Modeling Institute, the organization that offers the Advanced Financial Modeler program. I am a proud holder of the AFM. Preparing for the AFM exam made me a better modeler. If you want to improve your modeling skills, I recommend the AFM program podcast listeners save 15% on the AFM program. Just use Code Podcast.
Guest: Amber Johnson (18:59):
Yes. And this gets into the idea of forecast accuracy and bias. And so when I talk about accuracy, there's going to be a communal groan and then someone was going to say, well that could be MAPE or MSE or Mean Square, all of these different things. So when I'm talking about forecast accuracy, I'm talking about the summation of forecast minus sales, the absolute value over sales and the summation of all of those individual items. And so forecast accuracy, when you're looking at one item or a portfolio of items, certain durations of time, the cut of that data could be a million different things. You
Host: Paul Barnhurst (19:36):
Can slice and dice it a lot of different ways.
Guest: Amber Johnson (19:38):
Exactly. Now bias is not the absolute value. And so that is taking the kind of swings in both directions. And so why I like using forecast accuracy and bias together, and that'll kind of rope into how I'm answering the question is by looking at both, I'm seeing are we consistently over forecasting or overestimating or underestimating these ideas? Then you're making more meticulous decisions on your actions you take out of those things would be different. And so what I mean by that is, so if we are consistently over forecasting and we aren't tracking it, then eventually we have so much overstock in the warehouse now we have to get overflow, warehouse overflow storage and that costs money. And so back into the leading lagging. So by ignoring a little bit of bias each month or a little bit of over forecasting, that leads to big storage issues.
(20:31):
Conversely, if you're consistently under forecasting, you're underestimating now you're not ordering enough materials, everything goes down. And then now your ability for revenue has gone down quite dramatically. But now you're like, for instance, if people are going out in the circumstance of out of stocks, now you're creating a situation where clients are out of stock. And so client experience is kind of one of those immeasurable things aside from revenue is you could say that's a little bit of a pulse of client experience, but client experience, if they're consistently going out of stock and constantly under forecasting on all of this, now you're spending more in marketing to get new clients because your original client's lifetime value is going down because you're not actually catering to what they need. And so little tiny things if tracked well, you can get ahead. The canaries in the coal mine can kind of point you in better actionable directions if monitored well, but if it nets out, if you're a little over a little under and your bias is zero, you don't notice that there's something wrong. But if you look at your accuracy with your bias now you're like, well why are we at 80% accuracy? But my bias is zero. That means there's actually two humongous problems being created. But because you're only tracking bias financially, I'm set for now. So I digress. Well,
Host: Paul Barnhurst (21:45):
You mentioned a concept there when you talk about how, hey look, your forecast can be really accurate. That doesn't mean you don't have huge biases. And the example I give, I came from FP&A, so obviously variance commentary, you're always looking for those major items and you could show you're like, oh, we're within a thousand dollars of the forecast. Yeah, we missed revenue by half a million, but we had this huge under expense. That's just timing. So the reality is we have a big huge miss that's going to be a problem. But in theory, it looks like we're on. The favourite I had is, so I worked in a business travel and we had huge contracts. You can't recognise the revenue until they're signed. And one year we recognised two of them a couple months early, so we were like $20 million ahead of forecast and corporate couldn't understand why we were forecasting. To come in on Target, trying to explain it to 'em was nearly impossible. They're like, we don't believe you. I dunno what to tell you. It's a timing issue. Just trust us. And our CFO hated it. And this was a global company, we were a small part of it, but always mess with quarter of the earnings because 10 20 million can swing a percent here or there and you never know what that's going to do.
Guest: Amber Johnson (22:57):
Yeah, I do Looking at bias in court, different segments of time as well, looking at it, okay, one month, but okay, you said it was timing last month. So then you pull 2, 3, 4, 5 months, you're like, okay, well you've said timing every time, but we're consistently under. So that's when you start playing that game too, because there's been times you'll get ready for a a performance review and you're like, timing, timing, timing. And then the next month you're like, oh shit, it wasn't timing. I hope they don't notice. And so there's little teeny games that I used to play in corporate just to get past, nobody would ever do that play games. Me, huh. Never. I always have leadership
Host: Paul Barnhurst (23:30):
Was playing. I'm like, yeah, there's timing, but there's probably some favorability here as well.
Guest: Amber Johnson (23:35):
Yes, yes, yes, yes. Absolutely.
Host: Paul Barnhurst (23:37):
I totally get it. Anyone who's worked in corporate has seen games take place with numbers. We all know what they say. You can make numbers be whatever you want 'em to be. Lies. Lies and more lies. Alright. Never quite, you'd learned this on a modeling show, but here we are. What do you wish finance leaders better understood about logistical forecasting? And we'll go vice versa. Let's start with finance leaders. Well,
Guest: Amber Johnson (23:59):
Thankfully in small business land right now I am the finance and the logistics. So I really have to fight myself every day. But when I've worked with bigger teams, I think it comes down to if someone in logistics folks, we like to make mountains out of molehill quite a lot. And so sometimes they're very relevant and they need to be listened to and it's like, Hey, remember that thing that I said was going to happen? And then you guys said it wouldn't happen and now it's happening. Sometimes I wish there was. I think this all kind of comes down to organisational goal management of making sure we're all on the same page to what the goal is. But sometimes I think finance folks can underreact to logistical concerns until it's coming out, until it becomes a, it's on the p and l and I see it because they don't really believe it until it's actually on the PL, which I get sometimes we're a little crazy over in logistics land, but then they come back three months later and they're like, what is happening?
(25:02):
And it's like, brother, look at your emails from three months ago and you would know I told you in a PowerPoint and an email. And then similarly the other way around for logistics, it's like I think logistics could do a lot better at connecting everything to a financial, connecting everything to a finance or a dollar. So sometimes in out of stocks there's not really a good way to quantify out of stocks other than maybe lost sales or maybe lost customers or maybe I think a lot of times we lose track of the plot of out of stocks are so crazy and it's like, well, is it compounding the same person asking for the same thing every day? Or is it new people or what is the uniqueness of it? Or similarly, I think some KPIs in logistics land lose track of the plot and don't realise how they actually financially impact the business.
(25:48):
And so they're kind of glamour metrics where you're like, yeah, you can track productivity, but at the end of the day, if the person completed their job by 10:00 AM versus 3:00 PM but is paid for the full day, that actually didn't impact the finances at all. It's like what are you doing with the extra time of the day to actually make the dollar worth it? So I think in summation, logistics needs to connect their KPIs to finances. More finance needs to take logistics more seriously when they actually say, Hey, there's a big problem over here. That's what I think.
Host: Paul Barnhurst (26:18):
Got it. Biggest, you mentioned something there of really finance listening more when you say, Hey look, there's a problem. Just you haven't seen it in the p and l, doesn't mean it's not happening. Is that from your experience, do you feel like over your career finance hasn't listened when you've raised the red flag? Or what do you think causes that? I mean, why do you feel like that's one of the big issues?
Guest: Amber Johnson (26:40):
I think sometimes, depending on the organization's relationship with forecast accuracy, if the whole organisation thinks that the logistics or the sales forecast or the production forecast is garbage, they're going to keep leaning in on their financial forecast and ignoring all of the inputs that are happening in logistics land. And I've seen this at one or two companies and I don't have a bunch of companies to reference, but from my little experience with it where there's actual different designated teams, it kind of comes down to the higher level above both of these entities to say, I am going to be reviewing both of these forecasts and using them together to come up with a better answer. And I think sometimes financial forecasts and late assessments have stretch goals in them, whereas logistics forecast should be trying to get close to reality. Whereas I understand targets and budgets sometimes are stretch goals to pull levers to get the business in a certain place.
Host: Paul Barnhurst (27:41):
They're where what the board wants you to achieve. And sometimes there are things in there that are just say it like it is, they're wishful thinking.
Guest: Amber Johnson (27:50):
And so I think knowing the use of each of the forecasts and how they kind of work together but also have their independent goals and purposes, I think that breeds a weird little friction of, well, my forecast is better than yours, so I'm not listening to you. And it's like with both sides. And so I think logistics is very pessimistic and trying to fit into reality where sometimes, and this is my experience at Anheuser-Busch, sometimes talking to the sales folks, sometimes they land a broken clock is right twice a day. Sometimes I'm like, huh, they said this crazy thing and the crazy thing came true. So I think sometimes there's always something on the other side and you have to learn the variables that actually lead to those being accurate, which I know is a non-answer.
Host: Paul Barnhurst (28:37):
Bad financial models can lead to bad decisions or worse. So how do you minimise the risk of a bad model? You make sure the models you build are great Financial modeling Institute developed the Advanced Financial Modeler accreditation programme to help modelers like you. The AFM programme offers a step-by-step approach to building world-class financial models. The programme ensures that you know the best practises in model design and structure and will help you brush up on your Excel and accounting skills too. Be the one on your team to build great models. If you want to impress your boss and your clients, get a FMI accredited podcast listeners, save 15% on the AFM programme. Just use code podcast at http://www.fminstitute.com/podcast. Yeah, no, I mean I get what you're saying. There's real value in understanding those key variables between the two forecasts and being able to push down on them. But that's an experience thing. You learn over time. You learn what different businesses, different people you work with, where their biases, where their forecasting preferences are, whatever they might be. There are people that are always going to tend toward being optimistic. There are people that are always going to tend toward being pessimistic and different people that use different type of forecasting and some that you're going to trust more than others.
Guest: Amber Johnson (30:13):
Some forecast with their heart, some with math and science and some with magic. So really somewhere in the middle you figure out. But that's interesting to see how people forecast. Is it like that opens up a whole can of worms, especially if it's in Excel, you're like, that becomes a little crazy.
Host: Paul Barnhurst (30:30):
See, my budget is just a whole bunch of heart and magic. That's why I've always short details. Yeah, I want to make the much from training. Oh wait, I made this much. What happened? Yeah, yeah. Again, I was overly optimistic. See, I'm the sales guy now.
Guest: Amber Johnson (30:45):
Well, owning your own business, you have to be your own delusional sales guy. But then also living in reality. So managing my expenses pessimistically, but then managing my sales, my targets optimistically of like, I'm delusional. Everyone's going to love my service and I'm going to sell a million. So much so I have a whiteboard that has all my financial goals and she's delusional, but you kind of got to be so
Host: Paul Barnhurst (31:13):
She is also a female like Excel.
Guest: Amber Johnson (31:15):
Yeah, exactly.
Host: Paul Barnhurst (31:16):
I'm going to leave it there. You said another word. I don't want to get myself in trouble. All right. So how would you like to see finance, sales and logistics work better together? If you could offer advice to kind of make sure they're on the same page? I think that's a lot of area in models and forecasting and estimating, whether it be FP&A traditional three statement models, whatever it might be. I think it's an area that there's often a challenge, just kind of in general, any thoughts?
Guest: Amber Johnson (31:41):
Every business I've ever worked with, ever, ever, ever. It all comes down to goal alignment of what is the goal and the function of the business. What is the overarching mission of what are we all trying to achieve here? And it kind of has to come from the top, but has to be communicated all the way to the bottom. And I think Anheuser-Busch did a really good job of no matter where you worked frontline in a truck in the corporate office, you knew exactly what the company's initiatives were nationally, globally, whatever in your own wholesaler. And everyone was made clearly aware what their targets were and how they actually added to the full pie coming together. And they're very intentional with the way targets and goals are cascaded. And so that's where no matter, I don't care if you guys meet weekly, monthly, but every meeting should be starting with what is our actual goal and what are we solving for and what are each of us doing?
(32:36):
Because then the conversation doesn't become, you're doing a bad job, I'm doing a bad job. It's more of we have a problem to solve, what are we doing to solve it? And so that's what I do with most of the companies I work with is sometimes there's a situation where they're chasing too many chickens of, well, we have six different goals. It's like whoa, whoa, whoa. You can bundle 'em all up. What is the primary objective? Sometimes it's like I don't know what my actual goal is. And then secondary is sometimes the communication of it doesn't actually make it to the people who are actually rowing the boat in the direction. And so it's not the super most technical advice, but there's similar to when you're starting to model like the where are we making this truly, if you just settle on the why and what are we doing, I think brings a lot of alignment because then everyone is in the same boat, rowing in the same direction,
Host: Paul Barnhurst (33:22):
And you had to bring it all back to that peachy stuff.
Guest: Amber Johnson (33:25):
I know it's horrible.
Host: Paul Barnhurst (33:26):
The human side, you have to align the goals, you have to align incentives, you have to be on the same page for the why, and that really does solve a lot of working together. Take sales commissions as an example. That's something you model. If you model those incentives correctly so they're aligned with the overall strategy of the business, you're much more likely to be rowing the same direction, which helps with your modeling because if everybody's rowing in the same direction, working off the same assumptions, you have a better chance of achieving your targets that you've set. Especially if they're realistic targets, you have a much higher chance than if everybody's rowing in a different direction. So it's amazing how often the answer comes back to the human side of things. As much as we talk about the science of modeling and there's clearly science in it, we're humans, so why George W said all models are wrong, but some are useful.
Guest: Amber Johnson (34:19):
I think what people don't realise is you can set out with this is the problem I think we need to solve and here's our goal. And then as you're pursuing that goal, you might figure out that's kind of a dog shit goal. We actually need to tweak the goal because we're rowing towards it and I'm not seeing the anticipated outcome I'm thinking. And so I think sometimes it's good to not change your goals every week, but perhaps having strategic alignment when we start to see it deviate or reinforcing that. And that's where starting every meeting with that idea, it's hard to avoid because it's always up in your face.
Host: Paul Barnhurst (34:53):
There's a reason we recommend rolling forecasts and scenario planning and all those things with your model sensitivity analysis versus just having a point estimate. If you're working off the budget or if you're working off the original model, you never adjust it, you're probably going to have a lot of bias by the end and a lot of lack of accuracy as well.
Guest: Amber Johnson (35:12):
And then it's like, okay, did we lose track of the plot of what a forecast is for? And that is for our latest understanding of what the future holds. And so truly if you just set it and forget it for a full year, it's like was the exercise to just see how good of a fortune teller I was last year? Yeah, it is good to still look at, okay, how far out in the future can I plan? So there's still benefit to it, but I love the latest estimates because then you can see how far a plan can deviate throughout a year. But I digress.
Host: Paul Barnhurst (35:39):
No, I agree with you. There's budgeting and planning and we're getting it a little off on that whole topic. Like I said, a little bit of tenure, but I think there's definitely value in that whole course correction part. If you're just setting it and forgetting it, then why are you doing it? Yeah,
Guest: Amber Johnson (35:55):
Exactly.
Host: Paul Barnhurst (35:56):
Alright, we're going to move a little different direction here. I have a couple questions before we move into rapid fire where we get to put you on the spot. I know how excited you are for that. So favour, Excel shortcut. I know you have one. What is it?
Guest: Amber Johnson (36:07):
She is the control left bracket, the trace precedence. Learned that at the act of cell conference lover so much. I don't use her as much as I'd like, but I love her.
Host: Paul Barnhurst (36:17):
That is a good one to go back to the prior Yeah, the formulas. Yep. Control. Great. What's the most unique or fun thing you've used Excel for in your personal life?
Guest: Amber Johnson (36:27):
I think the weirdest thing was probably a grocery planner, but it was based on I was heavy into macro counting calories and macros and all that stuff, so I basically created a little baby database of all the foods I like and then I could with a DHD, I like to just eat one food all day long and so if it's pasta, I'm like, I would love to eat pasta all day long. So what I did was I would put each of the things I wanted on my grocery list and then I said, how many servings could I have per day before maxing out one of the macros? Like macronutrients, like protein, carbohydrates, fats or whatever. So then when I went to the store, I couldn't purchase more than a daily allowable max out of that multiplied by the seven days. So there was no chance of me ever overeating that thing and maxing out some of the other foods. So that's when I realised that even healthy habits can be disordered. You
Host: Paul Barnhurst (37:23):
Are a total nerd.
Guest: Amber Johnson (37:25):
I know. I don't know where that one. Yeah, I'm done with that now. Now I just eat what I want. So
Host: Paul Barnhurst (37:31):
I've always kind of just followed the seafood diet. It's my problem. I'm trying to get better, but my brother's pounding calories, so I say good for you.
Guest: Amber Johnson (37:39):
Yeah, I think there was a I'm into fitness. Fitness pizza in my mouth.
Host: Paul Barnhurst (37:44):
Alright, rapid fire. I love it.
Guest: Amber Johnson (37:46):
Here we go.
Host: Paul Barnhurst (37:47):
Here's how it works. I have a few new ones. We just added them in the last few weeks, so this is not the original list we started with. We modified every so often. You can't say it depends. You have to pick a side. Then at the end you can elaborate on one or two that I know there's nuance around because everybody likes to add nuance. So you ready?
Guest: Amber Johnson (38:06):
Yep.
Host: Paul Barnhurst (38:07):
Circular references? Yes or no?
Guest: Amber Johnson (38:09):
No.
Host: Paul Barnhurst (38:10):
VBA, yes or no?
Guest: Amber Johnson (38:12):
Yes.
Host: Paul Barnhurst (38:14):
Lambdas in financial models, yes or no?
Guest: Amber Johnson (38:17):
No
Host: Paul Barnhurst (38:18):
External workbook links? Yes or no?
Guest: Amber Johnson (38:21):
Never
Host: Paul Barnhurst (38:23):
Mouse for modelers. Can you use the keyboard? Have to be all mouse. All mouse. Do you have to use outside of pivot tables? Should it be all mouse? I mean all keyboard shortcuts. Not all mouse.
Guest: Amber Johnson (38:34):
Oh, oh, all. No. I would say let people use mouses mice.
Host: Paul Barnhurst (38:37):
Let people use mouses. Yes. All right.
Guest: Amber Johnson (38:39):
Yeah,
Host: Paul Barnhurst (38:39):
Models should always be print ready, yes or no.
Guest: Amber Johnson (38:42):
Not one page readable though.
Host: Paul Barnhurst (38:44):
Alright, so you need a one that works. Are merge cells ever acceptable?
Guest: Amber Johnson (38:49):
Yes. Vertically.
Host: Paul Barnhurst (38:50):
Alright. I had someone the other day that said, I coined the phrase whoever uses merge sales is going to hell. I was like, okay, I'll put you in the no category. So we get a little bit of everything on that one. All right. Should financial modelers learn Python in Excel?
Guest: Amber Johnson (39:06):
Not yet. Power query?
Host: Paul Barnhurst (39:08):
Yes.
Guest: Amber Johnson (39:08):
Yesterday. Power
Host: Paul Barnhurst (39:09):
Bi
Guest: Amber Johnson (39:10):
If they're bored or if the company has more than three departments. Yes.
Host: Paul Barnhurst (39:15):
Alright.
Guest: Amber Johnson (39:15):
Big company or the board.
Host: Paul Barnhurst (39:16):
Got it. What about, let's see, every financial modeler should be able to build a fully integrated three statement model, yes or no?
Guest: Amber Johnson (39:24):
Yes.
Host: Paul Barnhurst (39:25):
Alright. Will Excel ever die?
Guest: Amber Johnson (39:27):
No. Never.
Host: Paul Barnhurst (39:29):
Have you used AI to help you build a model in Excel?
Guest: Amber Johnson (39:33):
Yes, A little bit.
Host: Paul Barnhurst (39:36):
A little bit.
Guest: Amber Johnson (39:37):
All right. Like logic questions. Does this fit in this section of the thing? Not really build the whole thing before me.
Host: Paul Barnhurst (39:44):
Sure.
Guest: Amber Johnson (39:45):
What
Host: Paul Barnhurst (39:45):
Financial statement is most important for modelers? Is it the income statement, balance sheet or cashflow statement?
Guest: Amber Johnson (39:51):
I think most impact is income statement.
Host: Paul Barnhurst (39:54):
Right. What's your favourite LLM? Do you have a favourite? Claude copilot chat, GPT, other
Guest: Amber Johnson (40:00):
Claude, but I go with Claudia because of course she's a woman. Female. Got it.
Host: Paul Barnhurst (40:05):
I think you have a problem with mills in your life, but we'll bring that. I love my therapy show that I'm going to start next and we'll have that question. Yeah, they're fine. If you could pick one for all your models, you could only do one and you had to pick one. Would it be sensitivity analysis or scenario analysis?
Guest: Amber Johnson (40:23):
Based on my understanding of what sensitivity analysis means, and I think through our conversation, I have new understanding of what that means. I pick scenario analysis at the moment, but I think under a little bit more research I would probably pick sensitivity.
Host: Paul Barnhurst (40:40):
Here's the way I think about 'em. So I'll give my definition and when it comes to sensitivity, sensitivity is how much does an input vary by changing another input. So how sensitive is my overall cost of goods sold to whey protein prices changing change 10 cents, I got a 4% because it's 60% on my cost or whatever scenario to me is really if there's a recession, what happens to my business if there's government regulation? So they should have specific situation that you're trying to model. Do I expand into a new country by a joint venture or do I build a new manufacturing plant? Those are two different scenarios. So your model scenarios where sensitivity should really be, how sensitive is an input to basically an output?
Guest: Amber Johnson (41:23):
I would say because of the lack of clear data availability scenario analysis is what I'll trust more often, but if I had data available sensitivity analysis all day. Do you believe financial models
Host: Paul Barnhurst (41:35):
Are the number one corporate decision making tool?
Guest: Amber Johnson (41:38):
Yes. If done well,
Host: Paul Barnhurst (41:40):
I like it. Yes, if done well, so in other words, no.
Guest: Amber Johnson (41:43):
Yeah, I'm like yes. If used routinely and understood by who's using it. Are you putting all
Host: Paul Barnhurst (41:49):
Kinds of comedy off on that one? I'll let it go.
Guest: Amber Johnson (41:51):
What is your lookup function
Host: Paul Barnhurst (41:53):
Like? Choose vlookup,
Guest: Amber Johnson (41:55):
Index match X lookup something else. If I had to use one for the rest of my life, it would be index match, but X lookup, I still like why not index X match?
Host: Paul Barnhurst (42:05):
So you can look at larger and smaller, you got more options?
Guest: Amber Johnson (42:09):
Yeah, I mean I'm down, I'm down, I'm down. Yeah, I think index match because of the columns and the, maybe I'm just haven't understood how to do a horizontal and vertical in X lookup at the same time. And I think that's my own lack of understanding. So I think if I was to bridge that gap X lookup, but I don't think I will.
Host: Paul Barnhurst (42:30):
I have a video that does bridge the gap for two-way lookups. It's really not hard. Once you learn it, I'll send you the video, the whole idea. So I agree index match is easier for two-way lookup and any nerds out there, we're going to go there for a minute, so you're going to have to forget. So basically you do your X lookup, you do your lookup value, you do your first lookup array, right? So you're looking out horizontal. Then what happens for your vertical lookup is you do a second X lookup in the return array, you hook up that value, you do your lookup array, so it's going to be your vertical. Here you have your horizontal here, you do your vertical here, and then similar to an index, your return array is the map. So to think of it is basically your table. And so that's all you're doing is you're doing your third argument, your return array becomes a second X lookup or lookup one and two are your vertical or horizontal. You can do 'em in either order and then your last return array of that second X lookup is basically your index or your table for both, and then it will look it up from that and then you can do the rest of the X lookup however you want.
Guest: Amber Johnson (43:37):
That's fair. I would say if I'm not doing, in the past, if I wasn't doing index match match, I would use X lookup nine out of 10 times and
Host: Paul Barnhurst (43:47):
I totally get it. And a lot of people don't like that because it's confusing that you got to write a nested X lookup. That's not intuitive. How often do you nest a lookup inside of a lookup?
Guest: Amber Johnson (43:57):
Not
Host: Paul Barnhurst (43:58):
Really,
Guest: Amber Johnson (43:58):
But knowing you could do it is helpful because the XLE cups just have a lot of functionality that I like, so that's great.
Host: Paul Barnhurst (44:06):
Yeah, and XM match has some of that functionality, the smaller and larger and the search first to last. That's why I like the X lookup over the match is if you go the index route, it gives you a lot more flexibility. 99% of the time you don't need it, but it's really nice for those occasional where it matters. Yeah, there's our Excel tutorial for the day. That will be 59.99 for my Excel course. I'm just kidding.
Guest: Amber Johnson (44:28):
Yeah, click the link of my bio.
Host: Paul Barnhurst (44:30):
All right, there we go. Last question for you as we wrap up here. If the audience wants to learn more about you, kind of the services you offer or get in touch with you, what's the best way for them to do that?
Guest: Amber Johnson (44:39):
I personally love LinkedIn, but Amber Johnson is a pretty unsearchable name. So if you look up Peachy profitability or Amber Johnson, Western Michigan University, but regardless, youtube.com/am jamber is where I'm currently hosted at. Eventually pizza profitability will have its own YouTube, but that's where I do the weekly live streams and I'm continuing the conversation around technology and continuous improvement. So between LinkedIn and YouTube you can get what you need or crazy idea peachy profitability.com. There is a contact form there, so whichever or you're really brave, just
Host: Paul Barnhurst (45:15):
Try searching Amber Johnson. Good luck.
Guest: Amber Johnson (45:17):
Fuck kidding. Yeah, and it could be one of a billion different other people. Usually it's obituaries and arrests, so I'm like, I swear I have not died yet and I have not gone to jail, so probably not me.
Host: Paul Barnhurst (45:29):
Well, on that note, now that we've covered death, jail bias, Excel forecasting therapy, depression and anxiety, and peachy, we'll call it a wrap. Thank you for joining me, Amber. Enjoyed chatting with you today and I hope you enjoyed this and I know our audience will enjoy it as well.
Guest: Amber Johnson (45:47):
Good, I hope so. Thanks.
Host: Paul Barnhurst (45:50):
Financial Modeler's Corner was brought to you by the Financial Modeling Institute. This year, I completed the Advanced Financial Modeler certification, and it made me a better financial modeler. What are you waiting for? Visit FMI at www.FMInstitute.com/podcast and use Code Podcast to save 15% when you enroll in one of the accreditations today.