Tim Denman: Welcome, everyone, to the “How Retailers Can Radically Optimize Inventory with the Power of AI'' webinar, which is hosted by RIS and presented in collaboration with Hypersonix. I'm Tim Denman, editor in chief of RIS, and I appreciate you joining us today. As we all know, the retail supply chain is in the midst of a reimagination. Inefficiencies are causing significant inventory loss across the industry, leading to empty shelves, long delivery times, and disappointed consumers. Now more than ever, retailers must get the right merchandise to the right consumer, at the right location, at the right time, they can ill afford delays.
Today, we're going to explore how smart retailers are harnessing the power of artificial intelligence across their supply chains to ensure they're running at peak performance, and vital inventory is where it needs to be, when it needs to be there. With me today to examine how industry leaders are leveraging the power of AI to ensure peak supply chain performance, how to best optimize inventory across the enterprise, and some real-world examples of nimble supply chains in action are Amanda Luther from BCG, Ken Fenyo from Coresight Research, and Rama Rao from Hypersonix.
Before we get started, I’ll allow each to introduce themself and describe their role at their respective companies. Amanda, why don't you begin?
Amanda Luther: Great to be here with everyone today. I’m Amanda Luther, a managing director and partner with BCG. My previous BCG background was actually in computer science, so about 15-20 years ago I was playing with machine learning and AI, and frankly, it felt very academic. I joined BCG about 15 years ago and really started my career doing none of that — I was focused on driving retail transformation.
Then, over the past five or six years, those two things have collided where machine learning drives value with retailers. I've been driving what we call AI at scale transformation, implementing advanced analytics to drive business value across both operations and commercial use cases. I’m super excited to talk about inventory management with this group today.
Denman: Amanda, great to have you. Ken?
Ken Fenyo: Hi, Ken Fenyo. I'm the president of research and advisory at Coresight Research. We're a research and advisory firm that focuses on the intersection of retail and technology. We look a lot at the key trends, and supply chain is definitely a hot one these days, as well as how it intersects with AI and other technologies.
Previously, I was at McKinsey & Company, a consulting firm. Before that I ran a retail tech startup called U Technology, and before that I had run loyalty and digital at Kroger. I've been both on the retailer side and seen some of this live, as well as on the vendor side and the advisor side, so hopefully I've seen it from all angles.
Denman: That's a great background, thanks Ken. Last but certainly not least, Rao Rama.
Rama Rao: My name is Rama Rao, I'm a co-founder, chief product and data officer at Hypersonix. We are a young Silicon Valley company, which is focused on enterprise AI. Broadly for us, it means all the aspects of how data and AI can make an impact at the enterprise level. Our big focus area, which is very relevant to the topic today, is on inventory AI, revenue AI, and customer AI. These tend to be the big levers with which enterprises can make a difference in today's rapid volatile environment.
My background has been in data and decisioning throughout my career, starting from my PhD at MIT. I spent 10 years working there at MIT, and then subsequently in many Silicon Valley tech companies focused on data and decisions. Very excited to be here with this amazing team today.
Denman: This is a great panel, happy to have all of you, and thank you for joining us. We're going to jump right into our conversation with our great panel. Rama, obviously the retail supply chain is under the microscope right now, we all know that. Can you talk a little bit about the transformation that is needed? How can retailers best optimize their inventory across the enterprise?
Rao: Supply chain and inventory are probably arcane terms that the average consumer doesn't want to hear, but they've been hearing a lot more of in the last 12-18 months. Any store you walk into, you're likely to see a sign that says, "Oops, some of these items are not available because of supply chain issues or other factors beyond our control."
Now, the real question is about the factors beyond our control, not only for the customer, but funnily enough, even for the largest retailers because of the nature of how goods travel, how goods are created, and how they reach the end consumer. It has become even more critical in recent times, given the fact that any and all disruptions happening throughout the supply chain result in disruption and change at the consumer level. Retailers are in the middle trying to manage and work against what they can access and source, and what the customer wants. Consequently, it breaks down into these main buckets: ships, ports, containers, drivers.
If you think about it broadly, for these large global supply chains the backbone of that is stuff that gets shipped out. You have ships that are stuck in ports, ports that are congested because they are not able to unload them. Then, you have a lack of availability of drivers because of labor shortages that we are experiencing, not only in the U.S., but throughout the world.
Containers have been stranded in ports around the world, which means we are in the perfect storm of supply chain issues. This leads directly into inventory and how retailers can manage inventory. Essentially, now more than ever, it's become a big issue and something that everybody needs to grapple with, whether they like it or not. That's at least the context for the problem and we can go into how retailers can address it.
Fenyo: It's not often that you start seeing supply chains on the front page of most of the newspapers — it's become such a huge issue. When I'm out and about in my neighborhood, people ask me about port congestion. It's not something I had in the past, I'd have to say it's been interesting over the last few months. When we're talking to retailers, the main thing they'll say is, "Well, what's new?" The answer is always, "We're spending all of our time looking for containers."
It's really become an obsession that it's a dominant thing that people just can't get their goods in. It's emerged as a real challenge. It'll be interesting to see how it'll affect the holiday because on the one hand people have a lot of money, but on the other hand, will there be goods for them to buy? That's going to be a challenge for the economy over the next three to six months, at least.
Luther: Ken and Rama covered it well. The other side that we will come to as we get through this is, what are the opportunities coming out of this? Obviously, retailers are focused on the short-term challenges, "How do I get goods on the shelf in time for the holidays?" As Ken said they're looking for containers, but we're also seeing retailers use this crisis as an opportunity on how to avoid this going forward. As we talk about that, AI will be one of the levers that retailers are thinking about.
Denman: Rama, I’d like to pause here for a second, would you mind going over your four buckets again?
Rao: Absolutely, ships, ports, drivers, and containers, or however you want to think about it, are the backbone of the global supply chain. Think about what happened in the last 18 months – there were disruptions everywhere: The Suez blockage, the port shutdown in China, COVID-related impacts on the workforce. That all completely affects it. Once ships don't get to the right places, containers which need to move around, are stranded in the wrong places.
Subsequently, there is a labor shortage that we are seeing, not only in the U.S., but everywhere else. The differing ways in which the governments of each one of these countries are choosing to deal with COVID and the mandates they're putting together, means there are different reactions in different places. The zero-tolerance policy in one place versus mandates to follow in another place means that labor is completely imbalanced. That becomes a situation like the L.A. port, where there are 55 containerships waiting to get unloaded, stretching back all the way.
Finally, even when you get it down, truck drivers. COVID has disrupted our labor force, not only here, but everywhere else. These four components, and not only these, trickle down to the next level. You might have heard — and it’s funny we have to start thinking about this — there’s a pallet shortage on the wooden pallets inside the containers. A global shortage of pallets, and now the prices of pallets have gone up 10-fold, or something like that. That is really the backbone that we are talking about.
Denman: The pallet shortage is interesting; they should come by my house. There's a business that has 15 pallets outside every day. Anyone can take them and burn them in the backyard — they have them.
Obviously, COVID has been the linchpin for a lot of this disruption. There's been other factors, which Rama and the rest of us have touched on, whether it's pallets or the Suez Canal, etc. Amanda, can you expand upon that? What's going on in the industry right now that's causing all of these supply chain nightmares beyond the obvious, COVID?
Luther: Rama hit on a lot of these. It has to start with COVID because that's where the disruption began, where we saw a precipitous drop in demand for a lot of things, in March of 2020. Then, as the stimulus hit, consumer behavior changed. We saw it accelerate and increase above and beyond anything that could have been expected. If you had planned your supply chain for a precipitous drop, then all of a sudden everything accelerates, there's a huge mismatch between supply and demand that we're still dealing with.
What that's caused is this perfect storm where shipping demand is up two times versus supply. Ports are congested with weeks and weeks of lead time sitting in ports, equipment shortages, trucking rates are up 25-50% in the U.S. One thing we are seeing is great warehouse utilization. It's at an all-time high, if you look at the data on that. So you've got all of these factors on every step of the supply chain.
Add the labor shortages on top of that, and it’s hitting every step. Retailers are feeling labor shortages in stores, but that's going back to every single step of the supply chain. It's not going away anytime soon. We look at these trends and there's some pretty secular disruption — how people think about work, how they want to work — that's going to continue to disrupt not only in-store retail, but also supply chain factors.
One more that I would put in here is forecasting because it’s harder than it was two years ago. Companies are struggling to forecast what demand is going to look like. As soon as we think we've got a handle on it, Delta comes along, which changes and disrupts forecasts again. That's been a challenge.
The last thing, on top of all those pieces, is that companies are looking at this as an opportunity. This is both a threat and a reason for reconfiguring the supply chain, it’s a great opportunity to do that
That's probably the right long-term move, to reconfigure where you think about getting your goods from, how you think about selling them, and the full value chain, but those changes are also disruptive in the near-term. When all of those things come together, it's no wonder that we've got the challenges we're seeing.
Fenyo: It's a great point. The labor one is interesting. We know there's been a shortage of drivers and retail has been having trouble hiring. I had actually wondered if part of the challenge was that Amazon and others were hiring people into warehousing. Some of the people might otherwise go to retail, but warehousing is losing people and can't hire either. It's challenging disruptions on the hiring side, which is exacerbating all these other challenges.
If there are changes in how people think of work, that's going to mean a long-term rethinking in terms of how you manage your business. You can't just assume it'll bounce back to the way it was in six months or a year.
Rao: That's a great point on warehousing and the implications of labor. This is essentially where the ability to look at unified data and deploy the power of AI to digest all of that comes back. When forecasting gets more difficult because it’s continuously hit with events and situations that you do not anticipate – it's not only the ability to anticipate what the supply can provide, but the customers preferences are also rapidly evolving given what they're experiencing.
The inability to forecast means you have to then turn and look at what you can forecast, or what you can control. Those are the places where the solutions lie and where data and AI come in.
Denman: There's tons of challenges out there in terms of supply and the supply chain. As we all know, when there's a challenge, the other side of that coin is the opportunity. Ken, you talked a little bit about those opportunities, and now that retailers have to take a look at their supply chain, or look in the backend, open it all up. There's an opportunity to add in some functionality that we didn't have before. Can you talk a little bit about the opportunity to meet these challenges?
Fenyo: I agree that with challenges come opportunities. We saw at the beginning of the pandemic, a lot of retailers had to pivot very quickly into e-commerce and transition. While it was painful, it jumped them ahead a number of years from where they would be. The challenge we're seeing is that the supply chain hasn't caught up in a lot of ways – there's a lot of opportunity there.
Ultimately, thinking about the supply chain and how to make it more efficient goes back to the customer experience — a better supply chain allows you to deliver a better customer experience. We've been looking at this and came up with five areas retailers need to think about that will take the supply chain to the next level. I won't spend too long on each one, but I'll lay them out.
1. Intelligent demand forecasting. We've already started talking about this. About 60% of companies recently surveyed in the retail world still use spreadsheets to do their forecasting for last year's data. Then, what they would refer to as “their gut” on what that is, which basically means they're guessing on supply chains. This is an area that’s great for AI, which is made to take in not just current data, but also external data on weather and other trends that we'll see.
2. Customer-centric collaboration. The more you share data, the more you’ll collaborate with your partners. If you're a food retailer and collaborating with CPGs, not just on supply chain, but thinking about how do I get my goods? How do I limit out-of-stocks? How do I get my goods from the manufacturing, to my DCs, to my store?
Then, in more creative ways around pack size and how to support e-commerce with better marketing or packaging that's easier to ship. There's a lot of ways to change the game in a way that helps both manufacturers, vendors, and third-party logistics — all the players — by sharing that data and marrying it with better analytics.
3. Control tower of the future. This idea of one system that shares data and analytics. Think of what some have started doing with the blockchain, it's a much more transparent way of sharing data that everyone can see. For example, what in the system is good? Then, if it’s food, add things like sensors to make sure that it stays within the right temperature. Or where is that set of sweaters I need in the system, is it at the port or is it on a truck.
4. Last mile. This whole idea of changing the game in terms of how to get goods to the customer. That's only speeding up. We've gone from next-day, two-day or one week, to two-day to one-day, and now we have a range of companies trying to deliver in 30 minutes or less. The idea of speed and how to automate it to make it happen is increasingly important.
5. Sustainability. It’s a bit of a foundational element because within all this, sustainability has become a driver for consumer decisions. It's certainly top of mind. Particularly for Gen Z and younger shoppers. Shipping goods from Asia or Europe to the U.S., then putting them on trucks is not the world's greenest thing to do. It’s thinking a lot about not only how do you make it more efficient, but how do you make it more sustainable? This is an ongoing challenge where again, data and analytics and rethinking the problem is going to be increasingly important, and will create big opportunities. Frankly, while sustainability is great for the environment, it also drops money to the bottom line. That’s something people don't think about. For example, in grocery, putting in better refrigeration means you have lower energy bills, as well as being good for the environment. It’s a win-win to think about.
Luther: I love the sustainability point because it's one that on the one hand, we don't think about enough, but on the other hand, is so incredibly powerful. I see this amongst myself and my friends, and as we're recruiting Gen Z-ers, they care a lot about what we're doing with the planet. Like Ken said, there's such a win-win opportunity on the business side. It becomes a no-brainer if you can get more efficient with your supply chain. It's something that with data, analytics, and AI, can actually be game-changing.
Rao: I love the five points that Ken highlighted, especially the point about sustainability and business impact. We have worked, for example, with grocery customers and one component of sustainability is that there are several aspects to the broader commerce and retail opportunity. What happens when you have an uncertain supply chain and you're trying to manage inventory? One situation is having excess inventory. We think of scarcity, but you also have the wrong thing at the wrong place, and you have too much of it.
When you think about fresh and prepared foods in the grocery industry, 30% of prepared foods are going to waste, into landfill, or ending up lost somehow or the other. Now, what we have started to develop is using the power of AI to then say, "Hey, what is the right way to price a product like that?" Meaning it is good today, will it expire in a certain amount of time? You want to essentially sell more of it when it is good, such that number one less of it goes into the landfill. It’s a true, win-win-win.
It's a win for the retailer because you have less shrink, less stuff that you have to write off. It's a phenomenal win for the customer because it is a good product that they're getting at a great price. Finally, it’s a phenomenal win for the environment because you have less of the stuff going to the landfills. Think retail wear fast fashion in all of those situations where it is hot and then it's not. At that point, you decide rather than having it discounted, I want it off.
In all of these cases — the power of data and AI comes in — what is the right price at which to move the item where the customer feels like there is value and the sale is there so that you're not necessarily destroying value, either for the retailer or for the alignment. These are phenomenal examples of being able to look at customer preferences, price sensitivity, and also location. We've been doing this for several retailers that don’t necessarily have the same type of customer demographic. This is where the power of AI comes in.
You may need to do markdowns differently in the L.A. region compared to the Bay Area, compared to the Midwest. Being able to do that kind of analytics, and say, "This is the cadence at which you need to mark prices down." Such that you are moving the goods out at the right place, is a place where data and AI can come in. This goes back to the earlier theme, this is one way you’re able to react to the uncertain environment, which is having this ability to deal with excess.
Denman: To keep the conversation in that AI area, supply chain has always been a long-haul game. It takes months, and in some cases even years, between when you design a product, to when you can build it, get it home, and sell it. The supply chain has always been long and bulky. But now, we're trying to infuse this near-term agility and nimbleness into the supply chain. Rama, can you talk a little bit about that and how AI can help speed things up throughout the supply chain?
Rao: If it was not already obvious, everything that we spoke about indicated that the supply chain is a long game. You need to plan ahead. For that, you need to know what's likely going to happen. When you're in a world where you don't know either, then you can't plan appropriately. For us, the theme of our engagement has been to control the controllables. What can you control in an uncertain environment? What can you control in an environment where you may have more or less of what you need? This is the near-term agility, as we put it, or the short-time scale actions, and how AI and data can help.
We have a variety of things that we focus on with partners, collaborators, and innovation efforts. One is, what happens when you have excess? What happens when you have a scarcity? At the excess we have options like markdown pricing or ways to get the goods at the right place at the right time. The first is the lever of pricing, and the second is inventory rebalancing.
It is almost as if you now have many versions of the supply chain within your enterprise. You're either trying to rebalance inventory within locations or across channels, and you're seeing this with a lot of retailers. One thing that the larger retailers do is effectively rethink physical stores as a greater distribution center. If there is excess inventory sitting in the Midwest, where previously the e-commerce business shipped from my central warehouses. Now, imagine and rethink every physical location as a hub, which can also ship out because it has excess and it's closer.
Again, this hits the sustainability point also. You're shipping fewer items across longer distances and trying to meet local demand. This also goes to the heart of the inability to predict that less was needed in the Midwest. This shorter term agility helps iron out the kinks and balance the fact that some things can’t be predicted. That is one area where we're seeing a lot of interest and a lot of action. It comes out in two ways, rebalancing between your locations to say, "Hey, I'm meeting brick-and-mortar demand in one location because I have excess in another location. My internal supply chain is rebalancing it in the manner of days and weeks."
Next, refiling cross-channel by fulfilling e-commerce demand from one location to another. Markdown pricing is another. The third is potentially looking at assortment and rethinking it given a shortage in a certain area.
With some of our QSR customers, when COVID hit and customer patterns changed this was a big one. Interestingly, the change was coming from the customer less than from the supply chain, which is equally important for inventory. The customer's preferences switched immediately. What we were able to show with AI is everything from shifts in shopping hours, changes in shopping due to the curfew being lifted, or an increase in traffic.
When people were coming in, they came once a day. In the QSR space, as opposed to multiple meals, they made a one meal purchase and they were purchasing for the family. AI was able to show how purchase patterns and the nature of what they were buying was shifting.
A quick story: We were working with a casual restaurant who was very sports driven. There were a lot of different TVs that showed sports in the restaurant. They had been working with a trailing four-week view for all of the forecasting. This included what labor was going to be and what their order would look like. Working with them, we created an amazing machine learning algorithm that took different variables into account, including weather and sports data.
Within the sports data, they can actually dissect that there’s a Cowboys-Giants game and that they're both in contention for the playoff, making it a huge game this weekend. This can be run through the forecast. Now, the forecast starting point is vastly improved from the trailing four-week average. In the restaurant, the GM looks at it weekly, and uses intuition to recognize that it's a Cowboys game and determine what needs to be added to the forecast.
Within the first week they over forecasted by 30%. The algorithm was right on and the GM added to it, which is what he had always done successfully with his gut feel. This combination of machine plus man is the most powerful combination, but you've got to spend the time and energy to help the operators understand what the data and algorithm can do, and then what they can build on top of that.
Honestly, it’s too easy to get focused on the algorithm or come up with a new set of data that's going to be really cool. That's not what it is, it has to be translated through the people and sometimes we lose focus on that.
Fenyo: From my own experience, I managed Kroger’s relationship with a company called Dunnhumby, which was a joint venture to help analyze customer data. It was a real cultural shift. It can’t be understated how hard it was to start thinking from the customer-out, rather than making a customer-centric view around the data. It took years and required things from the CEO, down. Instead of excluding them from internal meetings, it became, "Don't come to this meeting unless you have data on the customer. I'm not going to look at the plan unless it's built around a view of the customer and what the data tells us."
It was the same thing with our brands and CPG partners. We didn’t want anyone to come in and talk about plans unless it was grounded in the customer, in a shared view of the segmentation, and all the things we were trying to do. It was a multi-year effort.
It’s a great point, it doesn’t just turn on and go to work. There's a lot of moving parts and touches a lot of the organization. It’s like Battleship, but it's actually now more like that Suez Canal example Rama had given – it's hard to turn it. It takes time and effort to get people that are accustomed to doing things a certain way for years to do it differently. It's important and it's often overlooked in how this gets implemented. Ultimately, tools that are easier to use and don't require data scientists to make all the decisions help because then it can be distributed out. That top-down leadership and emphasis is important.
Rao: That's beautifully said, I love the example. Ken, your point about cultural change and the live example is phenomenal. At the end of the day, when you think about trying to drive organizational change or have people think more holistically, decision silos are often in place because fundamentally behind it are data silos. Looking at my part of the business, I cannot see anything else. I'm making decisions that are right for inventory, marketing is looking at the right data, but it's only the marketing data. The merchants are doing the same thing.
What we have found is that data silos lead to decision silos. When trying to integrate and have everybody begin to see a unified view of the enterprise, unifying the data effectively becomes the right way to provide one unified view across the enterprise. This allows anyone to see if there’s an inventory issue – this excess inventory means that the marketer and pricing manager need to figure out markdown pricing. Inventory rebalancing means store operations need to be ready for new initial inventory coming from a neighboring store and they need to know how to deal with it, how to work with it, and that it’s coming every other day.
The organization now has to be not only agile, but also have the ability to react to the fact that there are other departments working together. At the heart of it, it’s a people, process, and technology exercise that needs to be mastered.
Ken, I couldn't agree with you more about the last point you made, which is outstanding. In this age of holding hands and working together, the last thing you need is three layers from data to insight. It should be that as a store manager I can make the decision or see the information so that I can react. I don’t have to wait for somebody to do translations or build a dashboard for me. What tools can help bridge that gap so that I'm seeing the information, insight, and prompts that I need in order to take the action as fast as possible.
That is becoming a need of the hour. Near-term agility means these decision silos are being broken down, people can react and work together with current, accessible data to work and react.
Denman: I love this conversation. The culture of some of these companies need to change in order to actually utilize the tools that they're being provided. That's something to work on for the future. Ken, this is pretty broad, what does both the near- and long-term future of inventory management look like?
Fenyo: That is a broad question. It's important to take a broad view of questions like that. It’s not just the inventory, the inventory isn’t the result of the input, it comes from what’s being sold, how much is needed, where it’s going to go, how much is actually on the shelf, and all those come together.
There are a couple things to highlight, trends in areas that might be interesting for the inventory side: The first is around hyper-localization. For a long time retail has had one plan, one assortment – that's what we've got, it maybe gets tweaked a bit locally. Increasingly, AI allows for much more localization, which shifts the way that assortment and allocations are thought of. What is needed at this store, in this neighborhood? Not necessarily looking at a broad region or the nation is going to be an important differentiator. The Internet has raised the bar with consumers. They expect what they want to be available, expect to have it right then and there, and don't have any patience for out-of-stocks or any of that. Thinking of inventory, not as a national problem, but at a store or a zip code level, is important.
Second, think about the shelf edge. This is another area where there are increasingly new data sources, such as computer vision, that adds in new views to AI. It's amazing how little understanding some retailers have of what's actually on the shelf. Think about groceries, for example, and the out-of-stock problems that come, sometimes retailers don't know how many are on the shelf. They know how many they shipped, how many they sold, but not what's on the shelf at that moment. That's a huge opportunity to flag the issue. Then, things such as prescriptive analytics offer up a solution.
The last one is this idea of sustainability, in the end it gets into on-demand manufacturing, which is a big trend. Not necessarily in food, but definitely in apparel, think about changing the game – not having things made 12 months in advance, or ordered and sewed in Asia or other countries and shipped here, but actually making items when an order comes in using robotics, AI, and other technologies. Then shipping them on short-term, whether that's for a wholesaler or a consumer basis. This is a big trend we're just beginning to see.
Denman: We've been talking a lot about inventory and supply chain problems. Nike and other big players have gone on the record openly discussing supply chain challenges and woes, which is somewhat surprising to be so open. However, it makes sense. What can the smaller players learn from the mega corporations like Nike? What could they learn from big brother to trickle down to their operations? The smaller, regional players, what can they learn from the big guys?
Rao: Pretty much anything that we have discussed, there is a scale factor to all of these activities that a large enterprise does. At the end of the day, go back to look very hard at how you can control the controllables. What is within your control? If you're trying to deal with the supply chain, which you do not have much control of, you're reacting to it. You can anticipate as much as you want, as well as you want, but at the end of the day, you are reacting more. In which case, we have dealers that focus on tier one, tier two and tier three enterprises – we see the spectrum. Our focus when we go with the smaller partners is to hone in on the ability to react.
An inventory problem is not necessarily an inventory solution, it could be an assortment solution, a pricing solution, an off-shore operations solution. The way to think about it is to broaden your mind to the fact that the solution lies in another department, and hence you can't react to it.
Second, look at the ways you can react to it because sometimes it's an excess problem, not a scarcity problem. How do you deal with an excess? We shared some examples on how to deal with scarcity or other situations where you can then rethink assortment.
At the end of the day, there's still the customer. The customer is experiencing this across everything. It’s seen in every Starbucks thing; items are not available due to supply chain issues. They're not surprised that smaller retailers are also experiencing it. When you come up with an ultimate assortment, or with substitutes and other things to meet the needs, the customer is not going to be surprised.
The question is, how do you increase agility to be able to rotate through to find alternatives, assortment, and local solutions? This is a leaf for everybody's book, such that you can react to and realize that this solution is not in the same domain as the problem, and focus on looking at it that way. The heart of it is going to be, in order to make those decisions and see what the right decision is, this is where data, machine learning, and AI come to play.
Unified data across all of the enterprise stitches together store-level data and sales transaction-level data, along with inventory, how to price items, etc. With that unified data, decisions can be made that span across the enterprise. That is a place where the power BI and data come in, allowing you to make cross-functional decisions and be able to have greater agility. That is the domain of the masses.
When you get a tool that does not rely on either an army of data scientists or statisticians sitting there to do it, then you have access to it. Size doesn't matter because a small enterprise can still be forward thinking. Investing in the right technology that allows a category manager or a merchant to be able to actually get the data, interpret it, and work on it. Hypersonix, for example, front and center is built around a business user. Our goal is that a technical person should not be needed to do interpretation. The solution is directly usable by the business user.
That's where the answer comes in. Which is to say, I have the bottle of AI, but I can react to it. Our markdown solution is used by the store manager – without thinking about AI they are leveraging the power of it to be able to say, "What is the right price that I can mark on something, such that I can move the right item at the right pace?"
Denman: I don't know if any of you guys are gamblers on the panel, but they're asking us to set the spread here, so to speak. In your predictions, how long is the supply chain crisis going to last? Season over under, I'll go first. By about early 2024, we should be back to normal. That's my over-under, I'll leave it to the panel to fill in the blanks.
Rao: I think at least two years, 2024. The only thing that I'd ask is, what is normal? We're going to stabilize. The question is, where are we going to stabilize? We have fundamentally changed many aspects of what we as consumers do, and what retailers do. That is going to be a stabilization. That's where we can potentially talk about all of these things shaking out. If I were to put my betting hat on, at least a couple of years.
Luther: I think we're going to start to see improvements sooner than that, but I also think there's going to be more volatility that’s here to stay, and that's going to run past 2024. I'm hedging my bets, that's kind of a gamble.
Fenyo: I was thinking about six months, initially. The ship count outside of L.A. and Long Beach went up again, so I might give them … six months. Everyone thinks it takes at least twice as long as I think it will. That being said, it will probably take a year to unwind the current issues we're facing.
Then, I agree. There's so many long-term disruptions that it's hard to know what the new baseline will be, but I don't think it will be quick. It's hard to unwind once these things get bad. It's even going 24 hours at the port of L.A., I don't know that it's really going to change anything in the short-term at least.
Denman: Great, I hope nobody pulls this webinar up five years from now, and the supply chain is still a disaster and we're all disqualified, but that's part of the fun. For now, thank you to everyone for listening, thanks to our great panel for participating, and a big thank you to Hypersonix for sponsoring the event. Stay safe out there and thank you again for a great discussion.