How AI is Revolutionizing Inventory Management

Just about anywhere you look, there are a hundred different AI tools. These have claims to do all the heavy lifting for you – from content creation assistance to helping you code, and whatnot. All of this is made possible because of an evolutionary leap in LLMs (Large Language Models). LLMs are basically just databases with a voice, an interface to chat with them. AI has existed for decades before this, and it has been doing a phenomenal job for us since then. Although, it took the LLM revolution for businesses to realize that AI is the future. And now everyone wants to jump on the band-wagon and make use of it.

As I said, AI has been with us for decades now. We have been using it for science all these years. From calculating positions of the stars, to looking through petabytes of data and finding insights, it has been helping us shape the future of technology. And such is also the case for a lot of businesses. And I want to talk about how it can help (and already helps to some extent) solve the biggest supply chain challenge for retailers, inventory management.

Real-time Demand Forecasting

Perhaps one of the most efficient and straight-forward uses of AI is forecasting inventory demand. There is a ton going on in that space, including our own AI application for Shopify store-owners, Extend Inventory Forecast AI.

The idea is simple, AI looks through years of sales data and accurately predicts how much sales a store will see for the next few months. But that’s not all, it then looks at that data and factors in the lead time for each product and helps you plan your purchasing.

The result, almost zero under-stocking and overstocking. It helps you be on top of your cash flow, and keep debts in check. A marvelous piece of technology that is simple, and in my opinion, a must have for every retailer, both online and offline.

Major retailers like Walmart and Amazon use machine learning and AI models to adjust inventory levels by the hour and this has reportedly reduced overstock by 20-30% in many cases while maintaining high availability.

Computer Vision for Shelf-Monitoring

Companies like Trueview and Standard AI have developed solutions that can be retrofitted into existing stores. What they essentially do is keep an eye on every single piece of stock you have in your store or warehouse and help you identify misplaced items. Of course, it also counts the inventory for you and keeps a check on all your numbers, in real-time. Isn’t that just awesome?

This tech was so good, that it even led to tensions between worker unions and companies like Amazon. A disruption that almost every industry will see due to AI in the next decade or so.

Gone are the days when this technology was reserved to the elite companies. There are many affordable solutions that even small retailers can afford. Saves hours of manual work, and, of course, saves a ton of money for the business owners.

Supply Chain Optimizations

My favorite of them all, is the sheer marvel of machine learning algorithms managing multiple warehouses. It is nothing short of magic. We have worked with businesses with 50+ warehouses spread across the States and these systems effortlessly plan and manage the supply chain, saving hundreds of hours and thousands of dollars for these businesses.

At its core, it is much like demand forecasting but with the added complexity of planning outbound shipments and managing order fulfillment. The key is to connect everything to a central hub and put these mind-bogglingly powerful software in control of your WMSs.

Dynamic Pricing Integration

Okay, from a layman perspective, this sounds a bit spooky. But it is not, it is actually quite the opposite. How many times have you wondered, while picking up a near-to-expiry yogurt bowl at your grocery store, that its price should be reduced? I have to eat the whole goddamn thing by Friday.

There is a solution, and it favors the consumer, while also benefiting the business. Humans are very bad at food consumption management. Every year, we waste tons and tons of perishable items. Milk gone bad, crops gone bad, a whole warehouse full of fish sticking to its death because it was not sold in time.

Apart from the economic losses, it also has an environmental impact. And that is why I also love this piece of tech. These AI engines help businesses manage pricing of their inventory and make sure that perishable items are sold for consumption well before they are nearing their expiry. So essentially, you would pay less for something that is due to go bad soon, and the business would save money by not losing the item for free (not free actually, they also have to manage the waste and they end up paying more).

Imagine the whole world of inventory being run, the billions of lives and resources that we would save from having this system is just nothing short of amazing.

What does the future hold?

Since the introduction of LLMs, more and more businesses are now realizing the ROI with using AI to manage operations. This has resulted in increased investment in the sector. And with that, coupled with the amazing ideas that we conjure up every day, we will soon see a lot of new AI tools that can help with inventory management. Here are my top 3:

1. Autonomous Inventory Management

– AI systems will increasingly make ordering decisions without human intervention.

– This will expand from current basic replenishment to complex strategic decisions about product mix and seasonal planning.

2. Predictive Maintenance for Storage Systems

– AI will predict equipment failures and optimize maintenance schedules.

– This will reduce downtime and extend equipment life.

3. Enhanced Integration with Robotics

– AI inventory systems will directly control automated picking and storage systems.

– This will create truly lights-out warehouses for many applications.

What’s the hurdle?

It all looks good on paper, but implementing these solutions is a challenge on its own. There are many things to consider, and you may feel overwhelmed if you were looking to do this for your business. Companies like us, Codup, help make these decisions by being your tech partner. We help you figure out these challenges and plan on overcoming them and making the best out of what AI has to offer.

In my experience, I have seen mid-market companies face a few different challenges. Here are some for you to consider:

1. Data Quality & Integration Issues

– Legacy systems often contain inconsistent or incomplete historical data.

– Many retailers operate with multiple disconnected systems (POS, warehouse, supplier portals).

– Data standardization across suppliers and partners remains problematic.

– Small/medium businesses often lack sufficient historical data for accurate AI training.

2. Infrastructure Costs

– Implementing AI systems often requires significant hardware upgrades.

– Many warehouses need expensive IoT sensors and networking infrastructure.

– Small retailers struggle to justify the high upfront investment.

– Cloud computing costs for real-time processing can be substantial.

3. Workforce Resistance & Training

– Employees often resist automation due to job security concerns.

– Training staff to work alongside AI systems requires significant investment.

– There’s a shortage of personnel who understand both retail operations and AI.

– Middle management often lacks confidence in AI-driven decisions.

4. Technical Complexity

– AI systems need constant retraining as market conditions change.

– Edge cases and local market variations are hard to account for.

– Integration with existing ERP systems is often complex.

– Many solutions are not truly plug-and-play, requiring extensive customization.

5. Regulatory & Privacy Concerns

– Data sharing across supply chains raises privacy concerns.

– Different regions have varying rules about automated decision-making.

– Some jurisdictions require human oversight of AI systems.

– Compliance requirements can limit system flexibility.

6. Supply Chain Volatility

– Recent global disruptions have highlighted the limits of AI predictions.

– Many models struggled during COVID-19 and subsequent supply chain crises.

– Building resilient systems that can handle black swan events remains challenging.

– International trade tensions create unpredictable scenarios.

7. ROI Uncertainty

– Benefits can be hard to quantify, especially for smaller operations.

– Long implementation times delay return on investment.

– Some early adopters have had mixed results.

– Difficulty in measuring indirect benefits like improved customer satisfaction.

8. Market Fragmentation

– Too many competing solutions without clear standards.

– Lack of interoperability between different vendors.

– Smaller vendors may not survive long-term.

– Difficult to choose the right solution among many options.

These challenges are particularly acute for small and medium-sized businesses, which often lack the resources to overcome them. But, there is always a solution, a middle-ground, an ingenious solution that can help solve it all. That’s what we do, as Codup, as humans, as product developers.

What’s your take on AI and inventory management? Share your thoughts with us on this LinkedIn post. Or why not have a chat with us about how your business might or might not be able to take advantage of this? Throw us an email, or book a time with our solution engineers. We are invested in the future of AI and inventory management and we want to know how you think we can do better.