AI-Driven Inventory Optimization As An Operating Model Framework

Santanu Boral is the associate director at Cognizant, driving SAP-led supply chain transformation, ERP modernization and AI innovation.

In healthcare and pharmaceutical supply chains, drug products become unusable after expiration dates are reached. Balancing demand volatility and replenishment uncertainty increases the complexity of inventory decisions. U.S. hospitals discard an estimated $3 billion in medications each year.

Life sciences organizations have too much inventory and still fail to fulfill customer demand. Traditional planning models often operate in silos, separating demand forecasting from safety stock decisions. As a result, organizations struggle to adjust inventory levels in response to real-time changes in customer demand.

Traditional inventory planning techniques are mostly reactive and fail to detect shelf-life alerts early enough to support proactive intervention, and this limitation is significant because customer-specific shelf-life requirements at different customer service levels vary by geography. A product may exist somewhere in the network yet have a very short shelf life or regulatory condition to fulfill a customer need. ​

​The core problem is not that traditional machine learning forecasting has become obsolete; rather, it’s that it’s frequently disconnected from the decisions it’s supposed to improve. So I want to present a framework that puts the ​implementation argument for a stronger operating model that connects prediction, optimization, shelf-life risk and execution into one governed decision loop.​

How The Framework Works

This implementable framework has four connected layers, each with its own defined capability, outcomes and owners:​

Layer 1 - The Data Foundation: This is the input layer, and it consists of master data (e.g., location, product, demand signals, lead time shelf-life attributes). It’s owned by the data and planning leads.