Is AI-powered inventory management truly more efficient?
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Artificial intelligence is entering the world of business at an unprecedented speed, particularly in the technology-related innovation areas, where it seems that not using AI raises more and more questions, like “Are we working with outdated methodologies?” or “Could our supply chain strategies be optimized with Artificial Intelligence?”
As with most areas, AI has lately become incorporated into inventory management solutions. However, one major issue has yet to be addressed: “Is AI capable of forecasting more accurately and finding better solutions to inventory management, or is AI just more confident in its mistakes?”
Prior to analyzing the effectiveness of AI technology within inventory management, there is a need to examine what is commonly referred to as artificial intelligence.
Artificial Intelligence is a branch of computer science that deals with developing systems with the ability to carry out tasks requiring human intelligence, such as information analysis, pattern recognition, learning from experience, and decision-making. Unlike traditional applications, which run using predetermined instructions, AI applications involve data and algorithms that make them learn and perform their functions over time. Practically speaking, almost all of it is based on a technology called machine learning, a type of artificial intelligence that learns from past experiences. The technology is applied to huge databases, where patterns are found, and based on those patterns, predictions are made. The more good data it is fed, the better it becomes at predicting. This is where it becomes slightly misleading.
What is artificial intelligence?
The adoption curve is steep. Most of the medium and large companies, according to industry reports, are either piloting or already using AI-based tools for demand forecasting and inventory planning. Software vendors promise that AI will:
Provide an analysis of millions of data points: historical sales, promotions, prices, weather, events, and competitor moves.
Generate demand forecasts at high levels of granularity, such as at the SKU, store level, and even customer-segment levels.
Continuously optimize replenishment and assortment decisions versus once or twice a year.
Studies and case reports from vendors also cite many instances of 20-50 percent reductions in forecast error compared to traditional statistical models. Along with these, moderate improvements in service level and stock optimization are commonly observed. On paper, this sounds quite impressive.
But two questions critical in their nature have remained:
How much of the inventory problem is actually caused by forecast inaccuracy?
What if the changes in the environment actually happen at a pace that is faster than what can be noted in the historical data?
AI adoption wave in inventory management
The best forecasting models eliminate no uncertainty; at most, they refine how the uncertainty is quantified. If AI improves forecast accuracy by 20–30%, then most of the variation remains. Demand is inherently volatile and partially unpredictable for categories susceptible to fashion trends, competitive shocks, or changes in the macroeconomy.
The key limitation is of a structural nature:
AI and Machine Learning learn from the past.
Inventory decisions commit capital for the future.
When the future indeed behaves differently from the past, even the best AI models degrade very fast. That's precisely what happens in cases of demand shocks, new competitor entries, social media-driven trends, or sudden changes in consumer preference. In those situations, AI-generated numbers can be precisely wrong: produced confidently and in an automated manner, but still misaligned with reality.
The problem AI cannot solve
One of the assumptions AI-based inventory systems might make could be: The best way to manage inventories would be to determine ‘the right’ amount of stock to hold, using forecasted demand weeks or months in advance.
It sounds reasonable, but in reality, this includes:
Assigning inventory budgets to forecasts that may quickly become obsolete.
Locking working capital into assets based on a guess, even a smart one.
Assuming responsibility for the total risk involved in error, stockout, or dead stock whenever reality differs.
Such a strategy can be effective in stable and predictable settings, but it is inherently flawed for volatile settings, independent of the sophistication level of the algorithm used.
The hidden assumption behind AI forecasting
Dynamic Buffer Management (DBM) presents a significantly different approach to managing inventory. Rather than attempting to forecast what will happen in the future with great specificity, DBM operates to dynamically respond to what's happening in the present moment.
The core principles:
Buffer targets are assigned to each item or item location; they are not specific optimal levels.
Actual consumption levels are constantly tracked.
If demand is greater than predicted, when the inventory level regularly approaches the critical zone (often depicted as the "red" area), the buffer is increased.
When consumption is lower, and the stock level is in the "green" zone, the buffer is reduced.
This approach:
Does not rely on long-horizon forecasts.
Adjusts parameters according to observed behavior.
Learns from and responds to changes automatically.
DBM is not an attempt to forecast demand; it is an attempt to adjust for demand as it occurs.
Dynamic buffer management - a different way of thinking
AI is extremely capable at some things that inventory teams tend not to utilize much. In particular, AI is good at:
Pattern recognition in big data: the identification of new trends, changes in seasonality, or regional variation that could be missed by human observation.
Strategic product assortment issues: identifying product categories that are growing, declining, and where product sets perform well when combined.
Suppliers and categories performance analysis - identifying suppliers with consistently superior performing products, reliable lead times, and strong margins.
These are strategic questions:
What do we need to carry?
What are some of the suppliers that we need to feel confident in and grow with?
Which regions/channels are entitled to receive which products?
AI's capacity to analyze massive datasets and identify correlations invisible to humans has proven valuable. However, when AI is used for granular, item-level ordering decisions in volatile demand environments, its fundamental limitations become apparent: AI struggles to adapt to real-time changes it hasn't been trained to recognize.
Where AI truly adds value in inventory management
There is clear evidence of the development and implementation of AI within a business environment, so we are clearly not making a decision on whether to implement either AI or DBM. No, we are clearly making a determination on how to split duties within a combination of these two functions:
Role division between AI and DBM
Use AI for:
Determine what product lines to grow or decrease.
Determine which product lines are under-performing and over-performing.
Uncover regional or channel demand behavior.
Facilitate long-range capacity planning and portfolio analysis.
Assortment planning & management
Use DBM to:
Determine and vary buffer levels according to actual consumption trends
Respond to changes to demand in a real-time manner without having to wait for a subsequent forecast cycle
Limit the effects of forecast errors on operations
Manage daily replenishment
Dynamic buffer management for operations
Let's consider a typical example:
An average company handles hundreds or thousands of SKUs. Then, some products underperform on a regular basis, falling short of expectations. About 20-30% of all merchandise contributes to tied-up capital, occupies warehouse space, and involves handling, yet it sells little.
When carrying costs are between 20% and 30% of product value, dollars invested in the wrong products quietly chip away at profitability. The use of AI to improve forecast accuracy, but fundamentally misunderstanding overall demand in specific product categories, only adds to this issue. In this manner, AI is locking dollars into slow-moving product inventory.
The dynamic buffer management system resolves this issue by automatically shrinking the buffers for products that consume less than their projected amount. Instead of having to wait for the next forecast cycle to adjust the buffer size for the item when its underperformance comes to light during the next forecast cycle.
The cost of forecast dependency
Often, marketing language blurs the difference between AI and human thinking. The key differences:
AI doesn't really understand. It doesn't understand products or customers, or markets the way humans do. It doesn't have intuition, contextual judgment, or common sense. AI simply finds statistical patterns in back data and projects those patterns forward
AI works very well in stable environments: when tomorrow resembles yesterday with minor variations. The system is great at finding very minor correlations in vast amounts of data and then making incremental predictions.
AI model breaks down during structural shifts. New competitors, regulatory changes, changes in customer behavior, or unplanned black swan events represent patterns that are outside the things AI learned from history. When the future is different from the past, AI confidently projects yesterday's patterns into tomorrow-usually with disastrous results.
AI is an analytical tool, not a crystal ball. Recognizing this distinction will be important for deploying the tool effectively. Where historical patterns are reliable-that is, where demand is stable, and markets mature - AI adds value. Where structural change predominates - that is, where markets are emerging, or industries are being disrupted - human judgment supported by scenario planning becomes essential.
AI Is Not Human Intelligence
Strategically harnessed AI applied to categories, suppliers, and portfolios, together with dynamically managing buffers, is much more effective when compared to using AI by itself or conventional forecasting.
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The honest answer:
By itself, AI forecasting is usually only slightly more effective than traditional forecasting methods.
Nevertheless, it retains the basic weakness of not always being able to predict the demand, even with the use of complex models.
