Logistics Rewired: Avoid Stockouts & Surplus With Better Inventory Planning

Ted Boeglin: Hello everyone, and thank you for attending today's webinar, which is about Avoiding Stockouts and Surpluses With Better Inventory Planning. I'm Ted Boeglin, and I lead Flexport teams who helped small and medium businesses grow and profit using Flexport technology platform, our logistics infrastructure and our global trade expertise.

Today we are covering the most requested topic from our customers, which is demand forecasting and inventory planning. So why does this matter? Well, because getting it wrong means that you lose money or lose sales, and getting it right as a competitive advantage for you, it means that you can sell more product at lower costs.

We are constantly looking to improve this forum, and it's so helpful when you give us feedback about the topics that we cover.

It might not be customized to your specific business requirements. And we always recommend that you reach out to Flexport, talk to an expert to discuss your particular situation. But with that behind us, let's now meet our guests who are going to be with us today. First, I'd like to introduce Alex Kopco, who is the Co Founder and COO of Forum Brands. Alex, welcome, thank you for joining us. And can you tell us a little bit about Forum Brands?

Alex Kopco: Yeah, thanks so much Ted. I'm really really excited to be here. As you mentioned, my name is Alex. I'm one of the Co-Founder Forum Brands and the COO. What we do at the company is we acquire small digitally native brands and we scale them globally to be big digitally native brands. Additionally, we recently launched a an inventory and demand planning tool called Crystal, which I'm really excited to talk about today in the context of how to make smarter planning decisions.

Ted Boeglin: Alex, I can't wait to dig into that. I was excited to see your launch of the crystal tool recently. I'd also like to welcome Pratap Ranade who is the Co-Founder and CEO of Arena. Pratap can you tell us what Arena does?

Pratap Ranade: Yeah, thanks Ted. It's great to be here. Yeah, Arena, we founded the company to basically make enterprise decisions autonomous. And the general premise is we've seen a huge amount of progress in AI, especially when you look at game playing and robotics. But then if we look at enterprises, a lot of decisions are still in a paradigm of like decision assistance and advanced analytics, and so we're founded to basically bridge that gap. We're super excited about today, we started with most of our work actually on the demand side. And as everyone on this call is no stranger, demand and supply are very deeply connected. So through that work we've been pulled into supply.

Ted Boeglin: Pratap, no matter where you start, supply chain will find you. So the last two years have got us that. Okay, so let's dive into some of the questions. Alex, I actually want to start with you. You have been so hands on with so many different brands over the last few years.

Just-in-Time vs. Just-in-Case Operations

And I think some of the buzz that's out there is that we see brands starting to question the wisdom of the just in time inventory model that was so popularized in the in the 2000s. In some argue that we're moving more towards a just in case inventory model. This is a question that we're getting a lot. How would you advise brands decide between those two models of just in time versus just in case?

Alex Kopco: Yeah, it's a really good question. And I think the term just in time itself is a little bit misunderstood. It's sort of just in time, to me and makes me think of this, you know, almost custom order I, the widget isn't produced until the point of demand and, but in a different context just in time, for example, in the context of a retailer is really about making sure that when you walk into a store, you know a shelf doesn't have 500 widgets, maybe only as five widgets and so, it's really more about ongoing replenishment making sure that there are always enough widgets to fill the shelf. But that, you know, you don't have to hold as many widgets in the in the back room or even in that location that state.

And so this notion of just in case versus just in time, I think are really interrelated. And it boils down to the topic that we're here to talk about, which is demand planning and demand forecasting more broadly. And look, to me just in time, and the way that we approached it Forum Brands is really about making sure that we are locating the right number of widgets products as close to our eventual customers as we possibly can. And that minimizes the period of which we might be out of stock. It also though conversely can help minimize the amount that we're holding in bulk in one particular space.

But as it relates to just in case, as we've seen, it is prescient in these times to make sure that you're holding enough inventory to weather, many of the supply chain shocks that we've seen. And so, it kind of boils down to a couple of factors that I'll talk about that, we think about, number one is working capital, if you've got the cash because you're a slightly larger business, maybe you have a specialized type of product, that doesn't change very frequently, you may want to stock up a bit more than you would normally be comfortable with, just in case. Just in case your supplier runs into a challenge, just in case, as we saw last summer, the port of LA gets backed up by 75 plus ships, and you actually can't get your goods off that boat for a month or two, which happened to us, and I'm sure many other folks.

But there's also this notion of, if your manufacturer is owned by you, if it's domestically located, if it's located to your eventual customers, maybe you don't need to have as big of a worry about this notion of just in case, just in time can still work for you, because you're in more control of your manufacturing footprint. So that's one big consideration is the working cap. The other consideration is, do you have the space and so? We have a lot of our sales on Amazon, and anyone who's sold on Amazon over the last two years has probably run up against the caps that Amazon will implement in certain brands and in certain categories. And so in many cases, you can't really hold just in case quantities of inventory because Amazon won't physically allow you to store that many goods at an FBA warehouse. So then the question becomes do you have access to? Or would it be prudent to take advantage of a 3PL or 4PL an asset light inventory storage opportunity, just in case, again, you want to weather these supply shocks. And really that boils down to your use case, it boils down to your lead times, your manufacturing footprint, your growth plans, and where your customers are as well.

And so, yeah, I think just in time, just in case, it is case by case. And I don't think that just in time as a concept is going anywhere. But what I think it will require particularly in times like these is an evolved level of thinking about balancing the risks, and the trade offs of being overstocked versus out of stock, and realizing those two the bottom line.

Ted Boeglin: Alex, it's so interesting to me, because Forum Brands has to do this across companies that you acquire a very diverse set of SKU's and products that probably have different characteristics, some with very stable demand curves, others with highly seasonal curves. How are you sort of mapping out the various SKU's that you have and choosing the right approach for the right product?

Alex Kopco: Yeah, we follow the data at the end of the day. Big data geek wouldn't be here if I wasn't. But no, I mean, really, we let customers basically tell us, with their wallets, with their behaviors, and in many cases with their phone calls and their emails to our customer support teams, what they're excited about, what they're not excited about. And so if we're starting to see traction, particularly on the more seasonal demand curves of these different products, we'll stock up in advance of that and we'll look for opportunities even if they're a little bit more expensive in the short term, to delight customers who are telling us that they want these products.

Those that are more stable for which we can generate a much higher probability, a much higher likelihood based model. By and large, what we're doing there is we're just holding a couple of extra weeks of inventory domestically. Again, to protect against some of these shocks, like when we saw the Suez Canal get blocked up by a big barge, I mean, those are things that you can't really bake into a model per se, right, that's a black swan event that hopefully will never occur again, probably it will. But yeah, it's really just a whether some of those shocks.

And so again, it's case by case, it's nuanced, and what I always encourage our brand managers and we also talked to a lot of sellers in this space, who are using utilizing our tools or who we have, you know, close connections with. I always advise, at the end of the day, you the business owner, understand the nuances in the context of your business better than anybody else. And so while there are great demand planning solutions out there, of course we've released one as well. At the end of the day, any software, any model, it is not permission to turn off your brain, it is not permission to ignore the context that you know to be true.

And so, again, if you have working capital constraints, if you have physical limitations, if you've got Amazon imposed caps, if you've got category headwinds that are coming your way, you need to layer those factors on top of that sort of baseline level of forecasts in order to make the, I won't say the right decisions, because I don't actually think there is a right decision, they're just shades of wrong, and you're trying to be as minimally wrong as possible at any given point in time.

Ted Boeglin: That is actually a beautiful segue. So, Pratap, we've talked a lot about how you help companies make decisions through leveraging data to create better predictions. And one of the things that we've talked about is how, you know, you have to create probabilistic models that create different scenarios. And the importance of keeping people in the loop on the decision making. I know that one of the tools that you use is simulation, and that you create sort of simulated worlds. Tell us why you use simulations and how you help intelligent people make better decisions using your forecasting tools.

Pratap Ranade: Yeah, absolutely. I think for us, a huge reason behind simulation was rooted in sort of a lot of what we've been seeing in the last year with kind of unprecedented volatility, right? We went from like relatively stable times to extremely uncertain times and you had a lot of us just challenging the question of like, can you just take a model that fundamentally is trained on something that happened in the past? And use that to forecast the future? And obviously, because we've all seen, like, there's limits to that.

But does that mean that you can't really use data and machine learning. That actually doesn't mean that's the necessary corollary from that. And so, we asked ourselves and a lot of the team, myself included, we started our lives as physicists. So we're used to thinking about things bottom up instead of top down. And we said, well, at the end of the day what's happening, right? I as a consumer, I'm actually, yes I'm aware, I read the news about CPI, but that's not actually affecting my decision making. I'm walking into a store, I'm looking at products, I'm seeing how they're priced, and I'm making a decision based on the context at that time for me at that day.

And a lot of those interactions actually are knowable from past data. There's almost like these forces of like, cross product elasticity where certain products substitutes, you know, certain people they live in certain areas and so you have different kinds of types of people living in certain places, they're not suddenly moving across the country. And so there's a lot that you can know about these little knock on effects or butterfly effects. And so for us constructing that sort of like, atomic picture of the world that like the customer product and store level, how these pieces interact.

Actually created the foundation for this simulation. And where we sort of really got excited is working with our partners who where, again, on the demand and the supply side at companies, there's a lot of rich expertise and things that they've seen before. But if you can now marry the human and the machine, you could say, hey, I think I'm going to have stock out issues on the West Coast over the next X months, probably going to be between 60 and 80% of where you expect it to be.

Normally, that might just be an excel top down model. But actually if you can feed that in, that's not a uniform 60% everywhere. And some of that is normal, where are you still going to have access, where are you still going to have shortfalls? And so we found that to be really useful. So it's almost like a, think about it as a level builder for a video game, but you get to do, you get to be the game designer. And so that's how we've been finding stimulation to find utility and the supply side of the world.

Ted Boeglin: Pratap, what are some of the most common variables that you see, you know, very sophisticated companies who are doing as well as they possibly can to forecast demand and plan inventory. What are some of those big variables that they're looking at that have help them improve the accuracy of their forecasts?

Pratap Ranade: Yeah, it's a good question. I think a lot of the shocking secret is, it's not necessarily a huge vast array of them, it's like the quality of them and the granularity of them. So what we focus on is getting kind of the basics right, at like a granular and high frequency level. So can you get daily SKU level, store level price correctly? Can you get that a little bit up the chain, so like the price to the retailer, or the price to the consumer. Can you get some of the other factors that do drive behavior, which is particularly on like, if products are advertised, so that marketing spend.

So a lot of the controllable factors are things that are knowable, like volume sold, price, location. And then there's some simple things that we add for contextual data like demographics, a lot of the industries we work in, whether actually does matter a lot local events and holidays, matter a lot. So we do pipe that in as well. What's been interesting is actually given, again, if we you take a side tangent, some of you who are AI nerds have probably seen a lot of the development for language models and image models, what we found is actually weirdly adding in unstructured data from an image, stranger can help you spot like, substitute products. And we know this all the time as consumers you walk down the aisle, you look at deodorants, you can look at the design and you can kind of get a sense of which one's marketed to you.

So recently, that's been one of the things that we sort of brought in. And specifically that's been helpful with products where they're newer products, there's less sales data, just to try and make a prediction off of very little data. But I'd say, the core attributes are getting the sort of basics right, updating regularly at a very like atomic level.

Ted Boeglin: Yeah, it's interesting for you to say that it's not like there's some set of keystone variables that we probably don't know about, that everybody should be incorporating, it's more about the frequency quality of the most essential information that helps to improve that model. Alex, I'm interested to hear your perspective on that question to, what are you finding to be the most important pieces of data that you're feeding into your model that helps you get sort of high fidelity decision making?

Alex Kopco: Yeah, it's, this is not a cop out answer. It is exactly what Pratap said. One of the layers that I might add to this is, I think most folks in this space are aware of their annual seasonality. I peak in Q4, I peak in the spring, or whatever it is. But one of the other seasonal components that we think about is market based cyclicality in whichever locale you're operating in. And so a great example is we own a an at home fitness brand. And as Elon Musk among others predict that we may be entering a recession soon, one of the really powerful levers, layers, factors variables that you can add to it is this notion of a recession, and then you can try to get smart and leverage AI how deep will it be, if it's this deep. And this comes back to the question of simulation, what Pratap and his team are really focused on too is like, how do you try to predict them? Like the impact that a recession, a mild recession, a moderate recession, 2008, like crisis level recession. What impact is that going to have on people's discretionary income. Will they cancel their gym memberships? What happened in LA, but how many? And to what extent? and those people who do cancel their gym memberships. Well there's another confounding factor here which is, we just came out of a global pandemic in which 100% of gyms on planet Earth shut down at the same time.

So the question is, are people who canceled their gym memberships in a recession, gonna go re-up their own fitness gear? Or are they just going to dust off what they bought in the pandemic? So can we expect a similar lift in our at home fitness brand for net new products? Or not? What is the resale market going to look like? So I think there are innumerable new factors and new contextual specific variables that we would want to be able to consider that the power of AI and machine learning is pattern recognition, pattern application, and the more granular you can be over a longer period of time, the better the machines can be at spotting those patterns and translating them into a human context that allows us to understand it more efficiently.

Ted Boeglin: So you're both doing this in a very sophisticated level. I am willing to bet that a lot of the audience is looking at this and going well. I can't use machine learning or AI, and I don't have a big team that's gonna model the recession impact on home gym equipment. So, another way that I like to think about it is, what are the phases that companies should go through as they're starting to mature to gradually get better at demand forecasting and inventory planning. So I know both of you have also existed environments where like, a spreadsheet is pretty sophisticated for where some companies are at. So help me walk through how you envision the phases of map of maturity that a company goes through and demand forecasting and inventory planning.

Pratap Ranade: Yeah, so I can jump in and start. I think what, you know, refers to Excel and Spreadsheets, they are one of the greatest software applications ever. So I think, incredibly powerful, incredibly flexible. And I think like Alex had said earlier, like, nothing is an excuse to turn off the brain. So spreadsheets is tools for the brain, I think for sure.

3 Phases of Company Maturity

What we simply characterize them into three phases that makes it easy, but the first one is almost the data foundation. So even just getting all of that information into a clean spreadsheet is kind of a beginning, and being able to say, hey, if I'm, you know, you look at like Google Sheets is actually scripting and formulas and API calls that you can make from Google Sheets. In fact, one of our simulators you can actually call from a cell via an API call. So like, you could use a simulator straight from Google Sheets. So it's like, it doesn't need to be a massive IT undertaking. It doesn't need to be overwhelming or intimidating. I think that's the first thing is there's a lot you can use from simple tools today.

And I think that's sort of the data foundation is just getting it all kind of labeled getting a catalog. But that might start as a set of network spreadsheets that might move into databases, or data lakes. But just even simple things like making sure that the pricing and revenue management team, the marketing team and the supply team have access to what each other is doing. It's surprising, but you might have someone undergoing a big promotion in an area where there's going to be a supply shortage and we just wasted more money. So it's sort of the data foundations.