ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Retail Merchandising Based on Analytics and Experts' Judgement

13 September 2017, 10:10 – 10:30


Submitted by
giuseppe craparotta
Giuseppe Craparotta (Università di Torino), Roberta Sirovich (Università di Torino), Elena Marocco (Università di Torino)
Retail stock allocation is crucial but challenging for verticals whose sales are difficult to accurately predict. The authors developed an innovative solution in the context of a high-end fashion application based on collaboration between artificial intelligence and human intuition. Each week, stores are assigned a budget based on current stock levels versus potential sales, and a list of SKU/quantity to order and release is recommended. Each store manager is given a time window to modify the proposal respecting budget constraints. The artificial intelligence optimally allocates stock based on the requests and the expected likelihood of sale minus cost of logistics, plus other management-defined constraints. A test successfully outperformed the control stores who relied on traditional headoffice-driven allocation. The retailer boosted sales, demand cover, and stock rotation with an impact worth an estimated 1M EUR margin/month. Moreover, the new system improved store performance and store managers' morale through non-monetary incentive-driven empowerment.
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