Suggestions On Stockout & Aging Inventory Probability Prediction [d]

TL;DR: Working on a retail project for a grocery supply chain with 10+ distribution centers and 1M+ SKUs per DC. Need advice on how to build a training dataset to predict probability of stockout and aging inventory over the next N days (where N is variable). Considering a multi-step binary classification approach. Looking for ideas, methodologies, or resources.

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Post: We’re currently developing a machine learning solution for a retail supply chain project. The business setup is that of a typical grocery wholesaler—products are bought in bulk from manufacturers and sold to various retail stores. There are over 10 distribution centers (DCs), and each DC holds over 1 million SKUs.

An important detail: the same product can have different item codes across DCs. So, the unique identifier we use is a composite key—DC-SKU.

Buyers in the procurement department place orders based on demand forecasts and make manual adjustments for seasonality, holidays, or promotions.

Goal: Predict the probability of stockouts and aging inventory (slow-moving stock) over the next N days, where N is a configurable time window (e.g., 7, 14, 30 days, etc.).

I’m exploring whether this can be modeled as a multi-step binary classification problem—i.e., predict a binary outcome (stockout or not stockout) for each day in the horizon. Also a separate model on aging inventory. Would love feedback on: • How to structure and engineer the training dataset • Suitable modeling approaches (especially around multi-step classification) • Any recommended frameworks, papers, or repos that could help

Thanks in advance!