Feb 7th | 3 min read
Jean-Martin Roux
These days, it feels like “AI” is tacked onto almost every supply chain solution. But not every warehouse challenge needs or benefits from artificial intelligence. For many issues involving static or structured data, advanced algorithms can actually do the job better than AI. Knowing when to use AI and when to stick with traditional methods is key to creating efficient and cost-effective warehouse operations. Let’s break it down further.
The Difference Between AI and Algorithms
At its core, AI is about systems that learn and adapt over time based on data and feedback. Machine learning, which is a part of AI, uses big datasets to train models that get better the more data they get. On the other hand, algorithms rely on set rules, logic, and heuristics to optimize processes. They’re often more straightforward, predictable, and efficient than AI models.
The main difference comes down to how AI and algorithms make decisions. AI usually offers a probability-based answer, looking at multiple data points to predict outcomes. This function allows AI to handle uncertainty and changes in warehouse operations. Algorithms, though, work by sticking to a fixed set of rules, giving a clear yes or no answer. For example, AI might predict when a SKU needs restocking based on trends, while an algorithm would restock once the inventory hits a certain level. Knowing this can help decide which approach is best for the warehouse.
AI shines when dealing with unpredictable or messy data—like forecasting demand in a volatile market—while algorithms are better for structured environments with clear rules. The issue is that many warehouse problems fall into the latter category but are often seen as AI problems when they don’t need to be.
Warehousing Applications: When Algorithms Outperform AI
1.Warehouse Slotting
Warehouse slotting determines the optimal location for storing products to minimize travel time and picking inefficiencies. This is a well-structured optimization problem where advanced heuristics and mathematical models perform exceptionally well. Slotting algorithms consider:
- ABC analysis (categorizing items based on demand frequency)
- Velocity-based slotting (placing fast-moving items in easily accessible locations)
- Correlated picking analysis (placing commonly picked items together to reduce travel distance)
For most businesses, predefined slotting rules outperform AI because the logic is relatively static and does not require continuous learning. AI might be useful if slotting needs to adapt dynamically based on real-time picking behavior, but in many cases, well-crafted algorithms provide 90% of the value with far less complexity.
A truly dynamic warehouse environment, however, might involve unpredictable order patterns, frequent SKU introductions, and real-time congestion factors that impact picking and replenishment decisions. If inventory profiles shift daily due to promotions or highly seasonal demand, AI-based slotting might provide additional benefits by learning from patterns rather than relying solely on predefined rules. That said, an advanced dynamic slotting algorithm that continuously recalculates based on near real-time data can often achieve similar results without the computational overhead of AI.
2.Replenishment Rules
Replenishment strategies define when and how inventory is moved from bulk storage to forward-pick locations. Here again, deterministic algorithms shine. Common replenishment techniques include:
- Min-Max replenishment: Stock is replenished when inventory falls below a defined threshold.
- Demand-based replenishment: Using historical order data to predict when a location will require restocking.
- Wave-based replenishment: Aligning replenishment cycles with picking waves to ensure stock is available before pickers arrive.
These methods are highly structured and operate within well-defined parameters. AI could be beneficial in detecting seasonal shifts or demand anomalies, but for most businesses, algorithmic replenishment strategies already provide the necessary efficiency and predictability.
In a more dynamic environment, where demand fluctuations are unpredictable, lead times vary significantly, and SKU velocity shifts frequently, AI-based replenishment models could help refine stock positioning. However, advanced algorithmic replenishment rules—such as integrating real-time order data with inventory turnover trends—can often provide similar levels of adaptability without introducing unnecessary complexity.
When AI Makes Sense
While algorithmic methods solve many warehouse challenges effectively, there are cases where AI brings added value, particularly in situations involving:
- Unstructured or complex data relationships: AI can find patterns in large datasets where traditional rules fail (e.g., identifying hidden correlations between SKUs and picking sequences).
- Adaptive decision-making: If a warehouse environment is highly dynamic, AI can continuously adjust slotting or replenishment strategies based on evolving patterns.
- Labor optimization: AI can analyze picker behavior and warehouse congestion patterns to adjust workflows dynamically.
Avoiding AI Overreach
AI is a powerful tool, but it’s not always the best solution. Many warehouse challenges, especially those with clear data and defined processes, are better handled with algorithms. Businesses should resist the urge to apply AI to everything, especially when well-designed algorithms can offer faster, more predictable, and more cost-effective results.
Rather than turning to AI for every optimization problem, it’s smarter to choose the right tool for the task. By understanding when to use AI and when to rely on algorithms, companies can stay efficient without adding unnecessary complexity or costs.
Interested in learning more about algorithmic slotting and replenishment? Schedule a consultation with our team.
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