Machine learning & AI in inventory optimization: practical applications

6 & 7. Machine Learning, AI, metrics, and KPIs in inventory optimization

6. Machine Learning & AI in inventory optimization 

ML models can analyze complex demand patterns as well as supplier performance. Instead of the user manually entering lead times for each supplier, historical lead times across different periods can be automatically calculated and incorporated into the inventory policy. This reduces manual effort and ensures that lead times reflect the most accurate and recent supplier performance data. 

It’s important to note, however, that beyond demand forecasting and supplier lead-time prediction, machine learning and AI play a limited role in inventory optimization, as most decisions are made based on predefined parameters like service levels, and order quantities. Inventory optimization often relies on straightforward calculations or linear programming approaches rather than complex ML models. 

Machine learning can support inventory optimization by generating alerts for stock levels that deviate significantly from targets - highlighting inventory that’s either excessive or insufficient to meet demand. While this isn’t pure AI, as it doesn’t involve predictive modeling or complex decision-making, these alert systems can monitor thresholds and flag items that need closer attention.  

7. Key metrics and KPIs for inventory management 

When assessing inventory management performance, you should consider the trade-off between service level and costs.  

After setting target service levels, you want to assess if you’re meeting those targets. Fill rate is one metric that can track this, or the amount of backorders or on-time orders are also ways to do this. 
 
The stock to sales ratio: this ratio monitors the relationship between inventory levels and actual sales, indicating if stock levels are appropriately aligned with demand. A low ratio may signal understocking, while a high ratio suggests overstocking. This metric guides inventory adjustments to avoid carrying costs for excess stock and supports alignment with demand patterns. 

A typical metric used is the inventory turnover, which measures how frequently inventory is sold and replaced over a period. A higher turnover indicates efficient inventory management but needs to be balanced with the risk of stockouts. Turnover is calculated as follows: Cost of goods sold divided by the average value of your inventory during the year. 

List of 10 metrics by Eazystock, although we would recommend to focus on fewer metrics.