Stop Guessing Games: Use Machine Learning to Optimize Inventory

How much inventory is too much? Or too little? What is the optimum inventory level for smooth running of operations? 

Many businesses haven’t found straightforward answers to these questions, despite an increased focus on inventory management.

In fact, getting inventory management right has always been a top priority for enterprises. But this isn’t as easy as it sounds. Why? 

Because this requires procurement to accurately predict demand during a period. Flaws in forecasting mean that inventory is often out of sync with demand. 

As businesses rethink their priorities amid ongoing economic uncertainty, cost monitoring and risk management have become equally important. To thrive in these conditions, procurement is expected to maintain the right level of inventory. This isn’t easy by any means, especially with changes in trade and tariff policies and a disruption-prone environment. 

So, how can procurement determine that the stock in the warehouse is too little or too much? How can they utilize available data to make this crucial decision? Can they leverage technology in this endeavor? Let’s find out. 

How Machine Learning is Redefining Inventory Management 

Supply chains generate a huge volume of data. This includes data generated from various sources such as suppliers, warehouses, distributors, retailers and point-of-sale data. Machine learning simplifies the huge task of consolidating these data sources and even detects the embedded trends quickly. These patterns can be fed into inventory management and other functions in the supply chain. 

Machine learning can conduct analysis and develop algorithms independently without a defined taxonomy or structure. By interrogating data under constraint-based modeling, machine learning can extract the most influential criteria that impact inventory, demand, production planning, risk management and supply chain optimization. These criteria can be used to make informed planning decisions and improve the overall network agility and responsiveness. 

Machine Learning Use Cases in Inventory Optimization 

Machine learning algorithms can automate several routine tasks in warehouse and inventory management. But the application of machine learning is not restricted to automation. These algorithms analyze data, predict demand fluctuations and dynamically adjust stock levels in real time. 

The integration of machine learning in inventory management allows procurement to shift from a reactive approach to a proactive, data-driven approach. Here are key use cases of machine learning in inventory optimization: 

1. Demand forecasting: 

Estimating demand is where it all begins. Machine learning algorithms can detect data patterns, identify demand signals and establish relationships within extensive datasets. In this process, they provide deeper insights into customer behavior and demand patterns. These insights help companies to accurately forecast demand, adjust stock levels in real time and respond dynamically to market fluctuations. Algorithms can also look at historical sales patterns, store or website traffic and other key metrics to identify peak seasons. 

2. Lead time prediction: 

Procurement must be able to accurately estimate lead times, which can be unpredictable during uncertain market conditions. By factoring in incoming transactions and several variables, such as product characteristics, seasonality effects, vendor attributes and origin-destination locations, machine learning algorithms can determine near-accurate lead times for components. 

3. Supply chain optimization: 

Machine learning can collect and analyze data from sensors on weather, location, congestion, etc. to gain live visibility of inventory movement. It can identify supply chain bottlenecks that can slow down this movement. 

4. Warehouse layout: 

Machine learning helps improve warehouse layouts by identifying fast-selling goods, redundant items and product movement patterns. This helps optimize the placement of inventory within the warehouse to reduce picking time. It can also streamline product returns by automating the sorting and restocking of returned goods. 

5. Automated replenishment: 

Machine learning systems automate the reordering process by monitoring stock levels in real time and determining optimal reorder points. These systems are designed to trigger replenishment when inventory falls below a certain threshold. Supermarkets, for example, use machine learning to track the inventory of perishable goods. The system automatically places replenishment orders with suppliers when stock falls below a predefined level. 

6. Predictive maintenance: 

Production machinery and equipment needs periodic repairs and maintenance. Machine learning algorithms can monitor machinery conditions and proactively schedule maintenance. This minimizes unplanned downtime, which can impact inventory management. 

7. Inventory auditing: 

At times, the actual inventory is not what the warehouse system suggests. Machine learning can identify such discrepancies and compare physical with digital inventory to spot the misalignment. It can cross-check data to look for inconsistencies between what was ordered, delivered and recorded in the inventory system. 

More Productivity and Accuracy with Lesser Resources 

The increasing use cases of machine learning suggest that businesses can safely say goodbye to manual workflows in inventory optimization. They can leverage machine learning to automate routine tasks in inventory management, interpret data generated from different sources and make well-informed decisions to align supply with demand. They can streamline warehouse operations to expedite the shipping process. And they can allow the human workforce to focus their attention on more strategic and value-adding tasks. 

Reliable demand forecasting algorithms reduce the level of buffer stock, which means manufacturing devotes less time to producing this stock. This also releases tied up working capital, which can be used more strategically. 

Simplify and automate your inventory management with GEP’s AI-powered solutions