Inventory Optimization Platform: Predictive Analytics for Supply Chain
Predictive analytics solution that improved inventory turnover by 25%.
GlobalTrade Logistics
Retail
Improved Inventory Turnover
Reduction in Carrying Costs
Decrease in Stockouts
Forecast Accuracy
Client Overview
GlobalTrade Logistics is one of India's largest distribution and logistics companies, operating 12 distribution centers nationwide and managing inventory for over 15,000 SKUs. Founded in 1992, the company serves as a critical link in the supply chain for major retail, FMCG, and pharmaceutical clients, handling approximately ₹2,500 crore worth of goods annually.
With increasing market volatility, shrinking profit margins, and growing customer expectations for faster deliveries, GlobalTrade recognized the need to optimize their inventory management processes. Their legacy inventory systems relied heavily on manual forecasting and static reorder points, resulting in significant capital being tied up in excess inventory while simultaneously experiencing stockouts of high-demand items. The company needed a data-driven approach to balance inventory levels, improve working capital efficiency, and maintain high service levels.
Project Summary
Industry
Retail & Distribution
Project Duration
12 months
Team Size
15 specialists
Technologies
ML/AI, Azure, Python, TensorFlow, Power BI, SQL
The Challenge
GlobalTrade Logistics faced several critical challenges in their inventory management processes.
Excess Inventory & Working Capital
The company was maintaining excessive safety stock levels across their distribution network, with approximately ₹350 crore tied up in slow-moving inventory. This excess inventory was consuming valuable warehouse space and negatively impacting cash flow and working capital efficiency.
Frequent Stockouts
Despite high overall inventory levels, GlobalTrade experienced frequent stockouts of fast-moving items, with a stockout rate of 8.5%. These stockouts were damaging customer relationships and resulting in penalties from service level agreement (SLA) violations, estimated at ₹3.2 crore annually.
Manual Forecasting & Planning
Inventory planning relied heavily on manual spreadsheet-based forecasting with limited consideration of seasonal trends, promotional events, or market dynamics. Forecast accuracy averaged only 65%, and the process was time-consuming, requiring approximately 320 person-hours monthly across the distribution network.
"We were caught in the classic inventory dilemma – too much of what wasn't selling and not enough of what was in demand. Our manual forecasting simply couldn't keep up with rapidly changing market conditions, especially during promotional periods and seasonal peaks. We needed a data-driven approach that could help us predict demand patterns more accurately and optimize our inventory levels accordingly across our entire distribution network."
Arjun Nair
Supply Chain Director, GlobalTrade Logistics
Our Solution
YugantarX designed and implemented a comprehensive predictive analytics platform that transformed GlobalTrade's inventory management processes.
AI-Powered Demand Forecasting
We implemented advanced demand forecasting capabilities using machine learning:
- Multi-algorithm approach combining LSTM neural networks, XGBoost, and statistical models
- Integration of external factors including seasonality, promotions, and economic indicators
- SKU-level demand pattern classification for targeted forecasting approaches
- Continuous learning from forecast deviations and market trends
Inventory Optimization Engine
We developed a sophisticated optimization engine to balance inventory levels:
- Dynamic safety stock calculation based on demand volatility and service levels
- Multi-echelon inventory optimization across the distribution network
- Working capital allocation algorithms prioritizing high-margin, fast-moving items
- Automated reorder point and order quantity recommendations
Integrated Data Platform
We created a comprehensive data platform to unify inventory insights:
- Integration with ERP, WMS, TMS, and POS systems for real-time data flow
- Data cleansing and enrichment pipelines for improved data quality
- Centralized data warehouse with automated ETL processes
- Supplier and customer data integration for end-to-end visibility
Intelligent Inventory Dashboard
We delivered actionable insights through a comprehensive dashboard:
- Real-time inventory KPIs with drill-down capabilities
- Exception-based alerts for inventory anomalies and stockout risks
- What-if scenario modeling for inventory planning decisions
- Mobile-optimized interface for on-the-go decision making
Implementation Process
Our approach followed a phased implementation to ensure rapid value delivery and change management success.
Phase 1: Assessment & Data Foundation (2 months)
We conducted a comprehensive assessment of GlobalTrade's inventory processes and data landscape, identifying improvement opportunities and establishing the data foundation for the predictive analytics platform.
Key Deliverables:
- Inventory process assessment and gap analysis
- ABC/XYZ classification of inventory items
- Data quality assessment and cleanup
- Data integration architecture and design
- Implementation roadmap and KPI definition
Phase 2: Data Platform & Integration (3 months)
We implemented the integrated data platform, establishing connections with all relevant systems and creating the centralized data warehouse for the analytics engine.
Key Deliverables:
- Azure-based data platform implementation
- ETL pipeline development and automation
- Integration with ERP, WMS, and other systems
- Data warehouse schema design and implementation
- Data transformation and enrichment processes
Phase 3: Predictive Models Development (4 months)
We developed and trained the machine learning models for demand forecasting and inventory optimization, incorporating feedback from inventory planners to refine their performance.
Key Deliverables:
- Demand forecasting algorithm development
- SKU segmentation and classification models
- Safety stock optimization algorithms
- Network-wide inventory optimization models
- Model training, testing, and validation
Phase 4: Dashboard & User Interface (2 months)
We designed and implemented the interactive dashboards and user interfaces that would deliver actionable insights to inventory planners and management teams.
Key Deliverables:
- Power BI dashboard development
- Exception-based alert system implementation
- What-if scenario modeling interface
- Mobile-optimized views and interfaces
- User acceptance testing and refinement
Phase 5: Rollout & Adoption (1 month)
We implemented the platform across all distribution centers and focused on ensuring adoption through training, change management, and continuous support.
Key Deliverables:
- User training and documentation
- Change management and process alignment
- Phased rollout across distribution centers
- Performance monitoring and system tuning
- Support framework establishment
Measurable Results
The inventory optimization platform delivered significant operational and financial outcomes for GlobalTrade Logistics.
Inventory Performance
- 25% improvement in inventory turnover ratio
- 32% reduction in inventory carrying costs
- ₹145 crore reduction in working capital requirements
Operational Excellence
- 42% decrease in stockout incidents
- 93% forecast accuracy (up from 65%)
- 85% reduction in forecast preparation time
Financial Impact
- 15% improvement in gross profit margin
- 28% reduction in inventory write-offs
- ROI achieved within 8 months of full implementation
"The inventory optimization platform has fundamentally transformed how we manage our supply chain. We've moved from reactive, gut-feel inventory decisions to data-driven, predictive approaches that have dramatically improved our working capital efficiency while enhancing customer service levels. The ability to predict demand patterns with such accuracy and optimize inventory accordingly has become a significant competitive advantage for our business."
Sunita Kapoor
Chief Executive Officer, GlobalTrade Logistics
Technical Architecture
The solution architecture balanced advanced analytics capabilities with enterprise integration requirements.
Architecture Components
-
Integration Layer
API-based integration with enterprise systems including ERP, WMS, TMS, and POS systems, enabling real-time data exchange and updates across the ecosystem.
-
Data Platform
Cloud-based data warehouse and data lake architecture for storing and processing structured and unstructured data, with automated ETL processes for data transformation.
-
Analytics Engine
Machine learning models and algorithms for demand forecasting, inventory optimization, and anomaly detection, with model management and retraining capabilities.
-
Visualization Layer
Interactive dashboards and reports for delivering insights to users, with role-based access controls and personalized views for different stakeholders.
Key Technologies
Integration & Data
Analytics & Machine Learning
Visualization & Interface
Security & DevOps
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