Adopting AI-driven inventory management systems is initially enthusiastic, and then there is confusion. There are no results, and people are unaware of why that is the case.
It is not that the technology is bad. In fact, it is quite good. However, there is a certain disconnect between what AI can do and what inventory management actually is. This is something that you should be aware of, and it can save you from costly mistakes.
What We’re Actually Talking About
When vendors talk about AI-based inventory management solutions, they mainly offer machine learning technology. Machine learning technology is an analysis tool that uses historical sales patterns, promotion analysis, seasonal patterns, and other variables to make predictions.
This is not an idle promise. Machine learning technology is indeed capable of analyzing vast amounts of information and providing accurate predictions. It can process millions of variables all at once. It can find subtle relationships within complex sets of information. It can quantify relationships between variables that would otherwise be unknowable.
But machine learning technology is based on the assumption that patterns observed in the past will continue into the future. This works well in stable environments. It does not work well in environments where patterns are suddenly disrupted.
AI does not understand your market. AI does not understand what drives customers to purchase the products. AI does not understand the context in which patterns are changing. AI is simply a pattern recognition tool.
The Confidence Problem
The AI models make accurate, confident predictions. The numbers are convincing. The interfaces are complex. The teams are confident in the predictions because they come from sophisticated algorithms.
Accuracy is not the same thing as precision. An AI model can make confident predictions that never happen. It will predict with decimal-point accuracy but be wrong by 40% in reality. The problem is not with the precision but with the confidence, which undermines the healthy dose of skepticism that uncertainty demands.
The traditional methods of forecasting at least had a clear indication of uncertainty. When a buyer made a prediction of future demand, it was clear that it was only an estimate. With AI, the complexity of the interface hides the uncertainty. The prediction is definitive even when it is purely speculative.
The Market Reality Gap
What are the actual causes of inventory problems for most businesses? You’re working with suppliers who have fixed lead times. You’re working with an order cycle that can’t change every day. You’re working with products where 30% of them are underperforming. You’re working with an ever-changing competitive landscape, market changes, and consumer behavior changes.
What can AI forecasting do in those circumstances? It gives you better forecasts of what’s going to happen in the future. It gives you better accuracy for those forecasts, potentially 25% to 40% better.
But the actual causes of the problems are not forecast accuracy. Increasing forecast accuracy from 65% to 80% does not address the fact that you’re still wrong 20% of the time, and that 20% of the time is where you’re getting most of your inventory issues from. It doesn’t address the fact that you’re still making inventory decisions based on forecasts and not actual demand.
The Volatility Challenge
Some products are more predictable than others. Some products have stable demand patterns and can benefit from improved forecasts. If you’re dealing with commodity-type products that have stable demand patterns, then AI forecasting can be beneficial.
But what about products that are fashion-driven? What about products that are trend-driven? What about products that can be significantly impacted by competition? Products that can be significantly impacted by social media?
In those types of markets, the fundamental problem of AI forecasting comes into play. AI forecasts are based on patterns that can change instantly. A competitor comes out with a disruptive product. A social media trend emerges. A change in regulation causes a shift in market dynamics.
The historical data doesn’t show these structural changes until after the fact. By the time your AI system adapts to this new pattern, the damage is already done. You’ve invested capital in inventory based on a forecast that assumed yesterday’s market, not today’s.
Rethinking the Entire Approach
What if the entire approach to forecast-driven inventory management is fundamentally wrong?
The conventional wisdom on inventory management dictates that we should forecast demand well, determine optimal inventory positions, and then order to those positions. The better we do at forecasting, the better we do at managing our inventory.
It all makes perfect sense.
But it assumes something: that the best way to manage inventory is to forecast demand and then manage inventory based on that forecast. What if we can manage inventory based on current demand instead of the demand we can only predict?
Dynamic Buffer Management is based on this idea. Instead of predicting what will happen and prepping for it, DBM uses flexible buffers and adapts them to what actually happens.
The process is simple: set up buffer targets for all items, observe actual consumption, and adapt the buffers accordingly. If actual consumption is frequently near stockout, increase the buffer. If actual consumption is frequently above what is needed, decrease the buffer.
This approach does away with the need to forecast the future. It learns from actuals, not from history. It adapts continuously, not after waiting for the forecast to be updated quarterly. Most importantly, it adapts to changing patterns automatically, without the need to learn from actuals first.
Strategic vs. Operational AI
Now, here is where this becomes applicable to real life. AI is not necessarily useless for inventory management. It is misapplied when used for operational decisions in dynamic environments.
AI is very good at strategic analysis. What product segments show promise for growth? Where are regional patterns diverging? What supplier relationships are associated with robust performance metrics? What product mix is associated with optimal returns?
These questions will benefit greatly from the pattern recognition capabilities of AI. You’re dealing with complex relationships in very large datasets. You’re seeking trends that develop over time. You’re making strategic decisions where 10% better understanding can add tremendous value.
Use AI in assortment management. Use it to guide decisions on which product segments to grow or contract. Use it in long-term capacity planning. Use it in supplier and category performance analysis. These strategic decisions play to the strengths of AI.
For operational decisions, like day-to-day order management, replenishment timing, and item-quantity decisions, use systems that can respond to today’s reality. Use Dynamic Buffer Management that reacts to actual consumption rather than predicted consumption.
The Cost Nobody Calculates
Typically, any set of products has substantial dead weight. In other words, 20-30% of products are contributing little or nothing to revenue and are consuming substantial capital.
Carrying costs are usually 20-30% annually. That’s expensive inventory that’s using space in your warehouses and capital that’s not being returned.
Forecasting doesn’t solve this problem; it makes it worse. You’re using past sales as a basis for investing in products that are slowly declining. By the time you notice that statistically your sales are declining, you’ve been carrying excess inventory for months.
And then you have AI forecasting. You’re using sophisticated algorithms that are giving you recommendations for inventory levels based on past sales trends. You’re not aware that past sales trends are just a manifestation of your products slowly declining. You’re using sophisticated forecasting techniques to invest in products that are underperforming.
DBM takes care of this situation automatically. If products are consuming less than their buffer targets, DBM will reduce those buffers right away. No need to wait for the next forecast cycle to identify underperformance.
Making the Right Choice
The answer is not to stop using AI. It’s to use it in the right way.
Use AI for those applications where it makes a real difference: strategic assortment planning, long-term portfolio analysis, supplier performance evaluation, and regional demand pattern analysis. These applications benefit from the pattern recognition ability of AI systems.
Use responsive systems for those applications where responsiveness to reality makes a big difference: item-level order management, daily replenishment decisions, buffer management. These applications benefit from responsiveness to reality rather than predictions of future demand.
This distinction is not arbitrary. It’s based on using the right tool for the actual problem you want to solve. AI is the right tool for solving problems that require analysis, where pattern recognition based on history makes a big difference.
