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Amazon used to be a lot more laissez faire about how Fulfilled By Amazon (FBA) sellers used their fulfillment centers. Sellers could send in inventory, and, while the space wasn’t unlimited, if their sales were not as forecasted they would simply pay long-term storage fees. Sure, if a seller’s inventory management was poor enough they would have their inventory storage limits reduced and pay higher storage fees, but this was just an incentive not to let things slide too much.
However, in 2022 Amazon reduced storage limits overall to the point where some FBA sellers had sales and catalog size impacted, and in March 2023 Amazon revised their inventory system. There is now an incentive for FBA sellers to be highly accurate with inventory management because Amazon will reward them with increased storage limits. Precision is a carrot now, rather than a stick.
In this article, we provide five strategic methods that sellers can utilize to optimize inventory management on Amazon.
1. Leverage data to anticipate demand, and regularly monitor and adapt
Achieving successful inventory management on Amazon requires a profound understanding of past demand patterns and the capacity to accurately forecast future demand. Seasonality, market trends, historical sales figures, competitor activity and planned promotions all play a crucial role in determining the trajectory of sales.
At Emplicit, we advocate for the analysis of multiple historical data points, encompassing previous 7, 30, 60, and 90-day sales figures. Our logistics experts factor in internal factors such as stock availability, marketing spend, promotions, and sales and margin targets, and external factors such as seasonality, Amazon trends, new category restrictions and market entrants. A comprehensive review of shipments in working, shipped, or receiving status is also beneficial. Striking a balance between what has been sold, what is available, and what's en route to an Amazon fulfillment center is key to precise forecasting.
Inventory management isn’t a static task; it requires constant vigilance and flexibility. FBA sellers should regularly review and modify their demand forecasts, adjust their replenishment suggestions based on demand shifts, and update their minimum reorder points as required.
Sellers should review sales daily, plan replenishment frequencies to suit their needs, and maintain appropriate inventory levels at Amazon. Weekly replenishments can help keep a seller’s inbound pipeline full, minimize out-of-stock instances, and account for unforeseen supply chain disruptions.
2. Prioritize items based on sales and profitability
Amazon’s organic and paid algorithms prioritize products with high sell-through rates. This means best selling products end up selling better. Focusing on high-performing items allows FBA sellers to reduce monthly storage costs, avoid aged inventory and the associated fees that Amazon imposes, and curtail the need for costly removal orders. And sales velocity is the quickest way to get Amazon to increase your storage limits. Concentrate on the 20% of items that generate 80% of sales.
At the same time, sellers should prune their catalogs by removing slow-selling items. These items negatively affect Amazon’s Inventory Performance Index (IPI) score, which directly influences the space Amazon allocates to a seller’s inventory in their fulfillment centers.
If sellers are tight on inventory space, as well as the best-selling products, they should prioritize products with higher margins until Amazon provides additional storage, and they should reduce marketing spend accordingly – something which necessitates a close relationship between inventory and marketing.
Ranking products by sales and margins, and calculating the storage space each product takes up will go a long way towards understanding and anticipating demand on Amazon.
3. Capitalize on Amazon's capacity monitor for space management
Amazon’s capacity management system is a new system for allocating inventory limits to FBA sellers and allowing sellers to gauge their inventory capacity at Amazon’s fulfillment centers. It also enables sellers to bid on increases to their inventory limits.
Previously, Amazon had restock limits which were updated weekly based on the seller’s previous 90-day sales. Restock limits were determined by Inventory Performance Index (IPI) metrics such as sell-through, excess inventory, and stranded inventory. However, because the restock limits were updated weekly, it was challenging to plan accordingly, especially heading into a peak season or if a seller was about to run a promotion.
With Amazon’s Capacity Monitor program, sellers are given a monthly capacity outlook based on the cubic feet of space occupied by their products in Amazon’s fulfillment centers and their IPI metrics. Amazon not only provides a current month outlook on available space; they provide an estimate for the next three months which can aid in the inventory planning process.
To take advantage of the new system, it’s imperative FBA sellers understand their product's physical footprint in relation to the allotted space Amazon provides (Amazon does still provide unit estimates). Knowing a product’s cubic feet and the product tier designation allows for effective planning of inventory replenishment. Exceeding space limits means overage fees from Amazon, however, if a seller knows they have a peak in sales coming up they can bid for additional capacity (in cubic feet). However, selling-through this additional inventory means Amazon waives those fees, so it’s a win-win.
At Emplicit, we have seen the capacity monitor program benefit our clients, with many clients seeing an increase in the amount of inventory they can ship in – likely due to healthy sell-through velocity and other IPI metrics. The program has fundamentally changed the way we approach managing our inventory on Amazon, so everything sellers do regarding inventory planning should be within the context of Amazon’s capacity monitor program.
4. Consider Amazon-specific packaging
Smart sellers should already be considering the impact of their product packaging on their FBA fulfillment fees. If the actual product size allows, sellers can generate significant savings by reducing the size of their packaging. Amazon’s Small Standard rates are 15-20% cheaper than Large Standard rates depending on weight, and Amazon’s Small & Light rates are 15-27% cheaper still than Small Standard rates. However, fulfillment cost savings are not the only reason to reduce packaging size, smaller packaging can significantly increase Amazon inventory cost-efficiencies.
With Amazon’s capacity management system providing inventory space based on cubic feet rather than number of units, the space each product takes up is now more important than ever. While larger packaging sizes can sometimes improve sales in brick and mortar retail, sellers should consider developing smaller Amazon-only packaging. This will not only reduce fulfillment costs, but allow more units to be stored in the same inventory space. The combined savings can more than offset the cost of a redesign and second packaging print run.
Additionally, smaller packaging may qualify sellers for Amazon’s Compact By Design badge. This helps brands stand out, and increases click-throughs and conversions. (We suspect there are algorithm tweaks for brands with certain badges too, but it’s difficult to prove.) Amazon-specific packaging can help with Transparency (anti-counterfeiters) and help combat unauthorized resellers.
While it might seem like a significant investment and not something the inventory team typically gets involved with, reducing packaging size is a long-term way for FBA sellers to optimize inventory management.
5. Ship from China straight into FBA with Amazon Global Logistics
Amazon Global Logistics (AGL) offers a streamlined solution for sellers whose products are manufactured in China. AGL eliminates the need to use freight forwarders who would usually receive a shipment from China, then split up that shipment and forward on to multiple Amazon fulfillment centers per the standard FBA process. Instead, sellers can book shipments directly with Amazon, complete the necessary export/import documentation, and ship directly to US, UK or European fulfillment centers – sending the entire shipment to a single fulfillment center.
If leveraged properly, AGL can save sellers thousands of dollars in warehouse and 3PL fees and reduce the need for inventory to be processed multiple times before it arrives at Amazon’s fulfillment center, meaning inventory gets where it needs to be quicker.
AGL offers two shipping options – Standard Ocean Freight and Fast Ocean Freight – with the standard option giving sellers the opportunity to either ship via a full container load (FCL) or less than container load (LCL). Shipping partial container loads with Amazon doesn’t slow shipments down versus other carriers because of Amazon’s scale. Amazon’s economies of scale mean that AGL can offer shipping prices from mainland China and Hong Kong that most sellers are unable to match. And Amazon’s expert customs brokers get products cleared through customs quickly because Amazon has a vested interest in shortening the time to market.
This one-step international shipping direct to Amazon was actually something we pioneered before the advent of this service from AGL – working with our client Shapermint and their manufacturers in China and logistics team to ensure packaging and shipments were FBA compliant. However, now AGL offers this service, it’s an even easier solution to a common challenge. We suspect AGL will roll out in other international manufacturing markets, but Amazon is tight-lipped for now.
The importance of expert inventory management
Amazon inventory management is complex and needs constant attention. Sellers can hire a fractional inventory specialist because this is not something that should be trusted to an Amazon generalist. If sellers get inventory right, it will keep pace with sales. But if they get it wrong, their inventory can become the main thing holding them back.
Evan Sherman is the director of logistics at Emplicit.
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Constructor's Eli Finkelshteyn shares principles for responsible use of generative AI, and an experiment.
This is an article about how to approach the use of ChatGPT for improved ecommerce experiences without breaking shopper trust in the process. You probably aren’t expecting Jurassic Park references, but I’m going to make one anyway.
There’s a line of foreshadowing early on where Jeff Goldblum’s character speaks about the complexities and ethics involved in cloning dinosaurs. He says something to the effect of, “Everyone got so excited that they could that they never even stopped to consider if they should.”
ChatGPT, and transformers in general (the technology underlying ChatGPT and a lot of the newer software that’s emerging in the search and discovery world) are incredibly exciting. One of the most exciting parts is how easy it is to build something with ChatGPT, and we’re currently seeing a glut of new software that does just that. Still, what isn’t clear is how to leverage this technology in ways that aren’t just gimmicks, but are actually helpful to people.
It’s clear to just about everyone that we’re in a hype cycle, and that’s why it’s so important for new technology being marketed as “powered by ChatGPT” or “powered by transformers” to prove its business value and usefulness to end users. Not to mix blockbuster movie references, but with great computing power comes great responsibility.
So it’s a good time to ask the multibillion-dollar question: how can technology companies use ChatGPT responsibly and in ways that actually benefit people in the months and years to come?
We’ve adopted some overarching principles internally at Constructor that will serve as our north star as we continue to innovate in this space. As we came up with them, we realized they could be useful as best practices for technology companies in general, and especially those like us who are serving large e-commerce companies. As more and more ecommerce leaders across B2B and B2C evaluate AI solutions in the middle of a hype cycle, we think it’s especially important to publish our principles publicly.
Our hope is that if we stick to these principles, then we’ll maintain our customers’ trust while innovating on new technology, and we’ll do this while doing the right thing for shoppers as well.
1. Experimentation is a prerequisite for ROI
Lots of companies can release a cool-looking new product and make claims about its efficacy, but it’s important for all of us to recognize that we’re early in our understanding of what applications of ChatGPT are genuinely useful to humans, and which are just science projects.
Why? Because the vast majority of those results are still unproven.
With ecommerce technology, for example, it’s important to consider whether a technology partner is going to actually deliver a product that has a legitimate impact on revenue or user happiness. Succeeding at that is ultimately a manifestation of extensive experimentation, including testing, failure, iteration, and re-testing — over and over again. Like most good things, data science takes time. It’s complex, and it’s nuanced.
Why is this so important for all of us in ecommerce tech to recognize? Because if we release a bunch of gimmicky technology and promise it’s the next big thing, then when we do come up with something useful, no one is going to trust us anymore.
The critical takeaway here is that any breathless proselytizing about most ChatGPT-based tech at this stage is counterproductive and simply not honest. If a vendor says they know for certain where the gold is right now, it’s just very unlikely to be true.
That’s the critical difference between a culture of experimentation versus one that’s only interested in shipping the “new hotness,” no matter the results. Before a product goes to market, the company that created it needs to be able to explain, and ideally prove, how it will bring ROI.
Is it just a novelty, or will it — in our case as an ecommerce product discovery company — actually help shoppers discover more products they want to buy in a way that’s more convenient, more natural, or more delightful than what they already have? And how do we know?
At Constructor, we’re excited to be experimenting with ChatGPT and large language models and transformers, both internally and with willing customers. We’re excited about the experiments we’re doing, and we hope the customers testing them are too, but we also want to be very clear that these are still just experiments. I’m providing a sneak peek at one experiment we’re very excited about below, but please read on before watching it:
Because here’s what we’re not doing: we’re not claiming we’ve invented the best thing ever. We’re not promising 10000% ROI. This is an experiment we’re excited about, but there is a real possibility that it doesn’t work out.Taking risks is what innovation looks like. The difference is that at the end of the day, our charter is to create real value (and ideally revenue) for our customers — and we won’t sell them a product that delivers anything less.
2. ChatGPT for ecommerce should be trained on ecommerce data and built for the needs of ecommerce
Ecommerce is full of unique problems and use cases for the ways people find products and content. For example, an add-to-cart and a purchase are both “conversions,” but they’re very much not the same type of conversion. Software that’s built for ecommerce has to understand the distinction, because it’s incredibly important to the KPIs that B2C and B2B ecommerce companies care about.
That’s the primary reason that we built Constructor’s AI core from scratch — the keyword-matching and vector search algorithms that work for general document search just aren’t the best technology to apply to ecommerce product discovery where you have rich feedback from users via their clickstream, along with zero party data like answers in a product-finding quiz. And the same principles apply when we apply ChatGPT to ecommerce.
The idea here is, let’s not try to boil the ocean. We don’t need to create Artificial General Intelligence that can answer every type of human query. We just need to create a very specific form of Artificial Intelligence that does one thing (helping shoppers discover products they’ll love, in our case) very, very well.
In order to return the most personalized, attractive results to a shopper’s query, ChatGPT needs to be augmented with AI models trained on ecommerce-specific data like clickstream behavior, average order values, margins, and product attributes. What’s really exciting here is that it’s the underlying technology itself that has the potential to completely reshape ecommerce. At Constructor, for example, we are working on using transformers to provide better product retrieval and filter out irrelevant results in search queries. It’s possible that this work may have the greatest impact on customer revenue, even more than the “conversational commerce” element of ChatGPT. But we don’t know yet — that’s why we’re experimenting.
(Image courtesy of Constructor)
Another advantage to training transformers on ecommerce data is that it also might mitigate some of the public concerns around ChatGPT’s accuracy. We only surface products for shoppers based on their own zero-party data and an ecommerce company’s own catalog. This heavily limits the types of wrong answers the system can give, and is about as accurate (and effective) as you can get. You may not be able to ask the ChatGPT solution at your favorite retailer about the year Albert Einstein was born, but it will be much better equipped to help you find a shirt you’ll love, or that one snack you tried and enjoyed, but can’t remember the name of. That’s a sacrifice I think most companies will be willing to make.
3. ChatGPT should build — not erode — your customer relationships
We’ll be releasing the full results in the next few months, but our team at Constructor recently performed a survey of 400+ U.S. online shoppers. We found that more than half of them were at least somewhat hesitant about using ChatGPT to find products on websites.
In our current landscape of third-party cookies and data breaches, that response is understandable. And ChatGPT could trigger a similar backlash if it makes shopping uncomfortable or intimidating. New tech can be scary, and it’s a fair concern that you might not want to ask your grocery store to create a shopping list of your regular purchases if it automatically adds sensitive items like gas relief medicine without asking you first.
We don’t have all the answers yet (as a company, an industry, or even a society) for how we’re going to solve these problems. Some of it, especially in this experimental phase, will be creating more transparency for the shopper. Expedia is an example of a company approaching this the right way, in my opinion. They recently launched a ChatGPT interface, and they’re very open about it being experimental and not trying to “trick” customers into using it. It’s a small but important step in building customer trust and delivering the value that leads to long-term relationships.
(Image courtesy of Constructor)
Questions will come up as we break new ground here. They’re important to talk and think about, and that work is the shared responsibility of retailers and the partners they work with. Ecommerce companies and their technology partners coming together to consider these murky waters — and being willing to think through new solutions to put customers first — will be absolutely critical.
A balanced approach
The amount of excitement and buzz around the (virtual) water cooler here at Constructor when it comes to ChatGPT can’t be overstated. Speed to market is a noble pursuit in this agile, volatile economy, and we are experimenting every day to figure out which of our ideas with ChatGPT and transformers will bring customer value and which require rethinking.
But we also want to balance that excitement with humility. When ChatGPT and transformers are raising legitimate questions from consumers, from companies, and even from others in the AI community, moving forward carefully and intentionally is equally important.
In this retail tech arms race, it’s the companies who widely release technology that doesn’t help their customers that have the most to lose. Ultimately, AI shouldn’t exist just for AI’s sake. Artificial Intelligence needs to yield real value if the companies using it want to be valued by their customers. We hope that our customers have come to trust us for doing right by them and their shoppers, but recognize that trust is something we have to constantly earn and maintain. My hope is these principles will help us and the wider technology ecosystem do exactly that as we embark on this next exciting adventure in AI.
Eli Finkelshteyn is co-founder and CEO of Constructor, an AI-powered product search and discovery platform tailor-made for ecommerce. This piece originally appeared on Constructor's blog, and was reprinted with permission.