Case Study: How a Retail Chain Reduced 30% Inventory Waste Using Decision Pulse

In the dynamic world of retail, excess inventory is more than a storage issue — it’s a profit killer. One leading retail chain, operating across 40+ cities in India, faced escalating losses due to expired stock, misaligned demand forecasts, and inconsistent store-level data. In early 2024, the company partnered with OfficeSolution to deploy its AI-powered analytics engine, Decision Pulse, with a clear goal: optimize inventory and reduce waste.

The Challenge: Disconnected Data, Reactive Decisions

Despite robust sales, the retailer struggled with demand mismatches across its supply chain. Their systems relied heavily on static reports, legacy ERP forecasts, and human intuition. These limitations created:

• Overstocking in slow-moving stores

• Stockouts during high-demand periods

• Lack of real-time alerts for perishable items

Inventory waste was costing the business over ₹7 crores annually.

The Turning Point: Introducing Decision Pulse

Decision Pulse, hosted at https://decisionpulsegenai.com/, is a predictive analytics platform that integrates structured retail data (POS, ERP, logistics) with external variables (seasonality, weather, footfall trends). Built with a Gen AI backbone and governed by business rules, it offers real-time insights for faster and smarter decisions.

How It Worked:

1. Data Consolidation Layer: Connected 12 internal systems and 3 third-party vendors into a unified data lake.

2. Predictive Models: Used AI to forecast SKU-level demand per store with 87% accuracy.

3. Decision Layer: Delivered actionable recommendations (e.g., “Shift 200 units of Item A from Store X to Store Y”).

4. Alerts and Automation: Triggered reorder thresholds and markdown pricing suggestions via Slack and internal dashboards.

The Results: 30% Waste Reduction in 10 Months

Within the first three quarters of implementation, the retail chain saw transformative results:

• 30% reduction in inventory waste across all 80+ outlets

• 18% improvement in stock availability during peak sales

• ₹2.1 crore saved in expired and unsold goods

• 25% faster decision cycle at the store manager level

Notably, store managers no longer waited for monthly reports. Instead, they received daily AI-driven nudges to optimize ordering, replenishment, and clearance strategies.

Human + AI = Smarter Retail

The success wasn’t just about technology. The retailer re-trained its inventory and merchandising teams to interpret insights from Decision Pulse. By embracing a "co-pilot" approach, staff could challenge, tweak, or act instantly on AI recommendations. This human-AI synergy drove real cultural change.

Final Thoughts

This case study proves that predictive intelligence isn’t reserved for tech giants — it’s reshaping core business outcomes for traditional retail chains too. With Decision Pulse, the client didn’t just cut costs — they unlocked a smarter, more agile inventory system.

Whether you're a small retail group or a nationwide chain, visit https://decisionpulsegenai.com/ to explore how Decision Pulse can reshape your operations — one decision at a time