Michael Hill has selected Impact Analytics to implement retail-first AI to transform its merchandise planning across multiple international markets [1].

This partnership represents a shift toward AI-native supply chain management for high-value retail. By automating complex forecasting, the company aims to reduce inventory waste and better align product availability with local consumer demand in the jewelry sector.

The deployment will cover Michael Hill operations in Australia, New Zealand, and Canada [1]. The company intends to use the technology to improve its forecasting capabilities and refine inventory replenishment processes [2]. These updates are designed to help the retailer create more localized assortments, ensuring that specific store locations carry the products most likely to sell in those regions [3].

Impact Analytics describes itself as the "AI-native leader in planning, merchandising, and inventory optimization" [1]. The integration of these tools is expected to streamline how the jewelry retailer manages its stock levels across its global footprint.

By moving away from traditional planning methods, the retailer can leverage real-time data to make purchasing decisions. This approach allows for a more agile response to market trends, a critical factor in the fine jewelry industry where trends can shift rapidly.

Representatives from the company said the goal is to transform merchandise planning through the use of specialized AI [1]. The transition focuses on optimizing the balance between stock availability and capital investment in inventory [2].

“Michael Hill has selected Impact Analytics to implement retail-first AI to transform its merchandise planning.”

The adoption of AI-native planning by a luxury goods retailer indicates a broader trend of moving supply chain management from reactive to predictive models. For Michael Hill, this reduces the risk of overstocking expensive inventory while attempting to capture localized demand across three different continents, potentially increasing profit margins by reducing markdowns.