Most Operations managers are asking the same question today: Should we invest in AI-based inventory management systems? The promise is certainly attractive. Presentations from vendors tout incredible statistics, such as 20% to 50% improvement in forecast accuracy, reduced stockouts, and optimized working capital. But what lies beneath these attractive presentations is not as clear and is certainly not being openly discussed.
Understanding What AI Actually Does
Artificial intelligence, as it’s used in a business context, is actually machine learning. This means that it looks at past data, determines patterns, and then projects those patterns into the future. If you give it sales data, promotional schedules, pricing changes, and other external influences such as weather and competitor activity, it can then make predictions based on that data.
It’s good at what it does because it can process a lot of data. Instead of a person able to look at hundreds of data sets, AI can look at millions of data sets and find patterns that a person wouldn’t be able to find. It can also find relationships between data sets that a person could spend weeks finding on their own.
But here’s what AI can’t do: it can’t understand. It can find patterns based on past data, and if the future is exactly like the past, then that’s great. But if there are fundamental changes in the marketplace, such as new competitors, changes in consumer behavior, or unexpected events. Then you can see that AI can’t do anything other than make predictions based on patterns that no longer exist.
The Adoption Reality Check
Industry data indicates broad adoption of AI for demand forecasting and inventory planning. Medium to large enterprises are piloting or implementing these solutions at a breathtaking pace. The value proposition is clear: better forecasts drive better inventory decisions.
However, two fundamental questions are seldom answered candidly. First, what percentage of your inventory problem is actually driven by inaccurate forecasting versus other sources? Second, what happens when market dynamics shift faster than your data can track?
In reality, most inventory problems lie outside the realm of inaccurate forecasting. They lie in areas such as high lead times, unyielding supply contracts, inflexible ordering schedules, and an unwillingness to adapt inventory plans quickly. A 30% improvement in forecast accuracy doesn’t solve these problems. You’re still committing to inventory weeks or months in advance based on forecasts of an uncertain future.
The Structural Limitation Nobody Mentions
The truth is, no matter how advanced, AI is trained on the past to make predictions. And if the future continues to resemble the past, all is well.
But if it doesn’t? If the world changes at a pace that renders the past irrelevant? That’s when things start to go wrong.
Let’s think about the last few years, shall we? How many businesses were there that saw changes in demand that their AI systems never saw coming? The rise of social media trends that came out of nowhere. The actions of their competitors changed the landscape overnight. Supply chain disruptions have made their historical lead times completely meaningless. Changes in consumer behavior occurred at a pace that was too fast for their traditional forecasting processes.
In those cases, the predictions made by their AI systems are completely wrong. They are automated, so they appear authoritative, but they are completely off the mark. The precision of the predictions is what’s misleading here. They are so precise that people believe them, but they are based on historical data that is no longer relevant.
However, demand volatility doesn’t necessarily diminish with increased accuracy. Fashion-driven products, trend-driven products, and competition-driven markets will always remain unpredictable. Moving from a 60% accurate forecast to a 75% accurate one only reduces variability by 25%. However, it is this variability that causes most inventory-related issues.
The Hidden Cost of Forecast Dependency
Traditional inventory management, with or without AI, is based on a fundamental premise: the optimal approach is determining what is referred to as the “right” amount of inventory based on predicted future demand. However, there are a number of issues with this premise. First, you’re investing in a forecast that will probably become obsolete very quickly. Second, you’re committing working capital to inventory based on educated guessing. Third, you’re assuming full risk for the outcome.
Improving forecasting doesn’t solve this issue if you’re fundamentally misunderstanding demand trends for a particular product category. AI may actually exacerbate this issue by providing recommendations to invest capital in products that don’t sell well. You’re using advanced algorithms to lock capital into inventory that’s not selling.
A Different Mental Model
Dynamic Buffer Management embodies a different mental model. Instead of trying to forecast future demand, DBM reacts to the present reality.
The idea is simple: set buffer targets for your items, not specific optimal targets, but rather targets expressed as ranges. Continuously monitor consumption. If items frequently reach critical low points, raise the buffer target. If items frequently remain in safe zones, decrease the buffer target.
This model doesn’t rely on forecasts of future demand. It reacts based on actual consumption. It learns from reality, not from history. Most importantly, it reacts to changes without waiting for the next forecast cycle.
DBM doesn’t try to forecast what will happen. It reacts to what is happening. The difference is enormous in a changing world.
Where AI Actually Delivers Value
So, is AI useless for inventory management? No. It’s just that we’ve been using it for the wrong thing: trying to apply it for detailed item-level ordering decisions in an environment where demand is changing fast.
But what about strategy? What are our growth and decline areas? Where do our demand patterns differ from region to region? Who are our top-performing suppliers? What products are driving our sales growth?
Those are pattern identification questions that involve huge datasets. AI can identify trends that no human could ever hope to see. It can quantify relationships that would take human analysis months to discover.
So, let’s apply AI to assortment management. Let’s apply it to supplier and category performance analysis. Let’s apply it to our long-term capacity planning and strategy.
But for day-to-day decisions like how much to order and when to replenish, let’s apply systems that respond to actual consumption rather than predicted demand. Let’s apply Dynamic Buffer Management for our order management.
Making Inventory Management Work: Strategy Over Prediction
Is AI-based inventory management more efficient? Not if it is used in isolation for operational decision-making in an ever-changing world.
AI-based forecasting is slightly better than traditional statistical methods. It suffers from the same drawback as all forecasting methods: the future is not necessarily an extension of the past.
To be efficient, inventory management must use AI for its strategic analysis and dynamic systems for its response to changing realities. AI is good for assortment analysis, supplier analysis, and portfolio analysis. DBM is good for order management and replenishment.
This is not about rejecting AI-based inventory management. It is about using AI-based inventory management where it works and avoiding the mistake of depending too heavily on forecasts, regardless of their precision, for operational decision-making.
The businesses that get inventory management right understand these concepts. They use AI-based inventory management for strategic analysis, not for operational decision-making. They use systems designed to respond to realities, not forecasts. They understand that managing realities is more important than predicting the future.
That is the truth about AI-based inventory management. The question is not if AI-based inventory management works. It is where and how it is used.
