Objective: Develop a solution using ML-based diagnostic and predictive analytics that identifies root causes of stock-outs and off-sale behavior and generates custom alerts to take preemptive action against lost opportunity.

Key Issues Addressed:

1. Current systems can track and capture Out-of-Stock (OOS) but lack accurate forward-looking forecasts.

2. End-to-end inventory visibility

3. "Lack of a system that proposes preemptive alerts and strategic changes."

How do we address your challenges?

1. An ML-driven predictive model-based solution is built to identify the OSA levels in the stores and thus identify underlying issues.

2. Provide competence in handling issues of inventory and shelf mismanagement by monitoring phantom inventory and safety stock.

3. An accelerator to actively generate prioritized alerts at the channel-store-SKU-daily level to minimize lost opportunity.

This implementation uses the following native Azure components: ADF pipelines, ADLS Gen 2, Azure SQL database, Azure ML & Power BI

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