How Machine Learning Transforms Inventory ManagementDemand Forecasting:
Machine learning algorithms can analyse historical sales data, taking into account various factors like seasonality, trends, and customer behaviour. This allows for accurate demand forecasting, ensuring retailers stock the right products in the right quantities.Optimising Assortment Planning:
Machine learning can assess large amounts of data to determine which products are likely to sell well together. This enhances assortment planning, helping retailers create complementary collections and maximise cross-selling opportunities.Effective Markdown Pricing:
Machine learning helps set optimal prices for products by analysing data on inventory levels, customer demand, and sales trends. Retailers can implement markdown pricing strategies that clear excess inventory while protecting profit margins.Streamlined Replenishment:
By using machine learning algorithms to assess stock levels, retailers can automate replenishment processes. This minimises stockouts and overstock situations, ensuring customers find what they want when they want it.Personalised Recommendations:
Machine learning can analyse customer data and shopping behaviours to provide personalised product recommendations. This not only boosts sales but also helps in inventory management by aligning product suggestions with available stock.Dynamic Stock Replenishment Plans:
Through machine learning, retailers can create dynamic stock replenishment plans. These adapt to real-time data, such as the popularity of products, lead times
from suppliers, and demand fluctuations, resulting in more accurate restocking schedules.