$2.4M Saved Annually Through AI Demand Forecasting
Transforming Supply Chain Planning with Intelligent Inventory Forecasting
Client Overview
A large US distribution and logistics company partnered with Prakash Software Solutions (PSSPL) to modernize inventory planning across its multi-site distribution network.
The company manages more than 4,000 SKUs across several distribution centers, serving customers whose demand patterns vary widely by region. As the business grew, inventory became harder to control: some centers held months of excess stock while others ran short repeatedly, triggering emergency orders and delayed deliveries.
Forecasting and replenishment relied on spreadsheets, historical assumptions, and manual decisions. That approach worked when operations were smaller, but it could not keep pace with a growing, increasingly complex supply chain.
The client needed a forecasting approach that gave clearer visibility into inventory movement and supported faster, more accurate replenishment.
Industry
Distribution & Logistics
Function
Supply Chain Planning
Location
United States
Project Duration
14 Weeks
Services Provided
- AI-powered demand forecasting platform development
- Multi-location inventory planning optimization
- Dynamic safety-stock management
- Cross-location inventory transfer optimization
- ERP integration and workflow automation
- Supply chain reporting dashboard development
Technologies Used
Forecasting & AI Models
XGBoost, Facebook Prophet, ARIMA
Data Processing
Python, Pandas
Cloud Infrastructure
Microsoft Azure, Azure SQL
Data Integration
Azure Data Factory, Dynamics 365 APIs
Reporting & Analytics
Power BI
Frontend Development
React-based Planning Dashboard
The Challenge
Inventory management through several distribution centers was the biggest challenge facing the organization. The demand for different products varied according to location-specific tastes, habits, promotional strategies, and climatic conditions. In fact, at times one distribution center would be overloaded with inventories of a particular product while the other lacked the same product.
This manual method depended a lot on the skills of the buying department and operated reactively, addressing problems after they happened instead of trying to solve them in advance.
Some of the major challenges included:
- Managing 4,000+ SKUs across multiple distribution centers
- Regional demand swings and seasonal buying patterns
- Inconsistent supplier lead times that disrupted replenishment planning
- Excess and slow-moving stock tying up working capital
- Frequent emergency orders and expedited freight costs
- Limited visibility into cross-location inventory opportunities
- Manual forecasting workflows that no longer scaled
The company needed a process that combined inventory trends, supply chain activity, market signals, and site-level inventory movement into proactive, well-informed decisions.
โThe hardest part of this project was balancing orders across locations without over-ordering or running short. The forecasting models had to reflect the companyโs day-to-day operations โ not just historical sales patterns. We worked closely with logistics planning and operations managers throughout, so the recommendations were practical, easy to understand, and genuinely reduced excess inventory costs.โ
Manish Langa
AI Practice Head, PSSPL
How PSSPL Helped
A solution for smart demand forecast and replenishment was developed taking into account the actual processes of the clientโs business and logistics.
The algorithm was based on sales history for three years, current inventory levels, lead times from the supplier, demand trends, promotion plans, and external demand indicators to provide replenishment recommendations for SKUs.
It was supposed that the solution would work within the Microsoft Dynamics 365 environment of the client, not involving any modifications to the architecture or ERP system.
Outcome: The solution allowed avoiding unnecessary expenses, balancing the inventory better, and increasing the level of trust in the supply chain staff.
Key Intelligent Document Processing Capabilities
Feature
Description
Multi-Site Demand Forecasting
Generated location-specific demand predictions across multiple forecasting horizons
Dynamic Safety Stock Optimization
Calculated inventory buffers using real-time demand variability and supplier performance
Cross-Location Inventory Visibility
Identified opportunities to transfer stock between facilities before placing supplier orders
Automated Replenishment Recommendations
Delivered daily inventory planning recommendations for buyers and planners
Exception-Based Alerts
Flagged shortages, demand spikes, and inventory risks proactively
Buyer Review & Approval
Allowed planners to review and adjust recommendations before execution
Forecast Accuracy Monitoring
Continuously improved forecasting performance using operational feedback
Implementation Journey
We delivered the solution in four phases, each designed to reduce manual effort, fit the clientโs existing systems, and build the teamโs confidence in the recommendations.
Discovery & Supply Chain Assessment
To better understand the replenishment process, we reviewed the replenishment processes, analyzed suppliers’ performance and inventory challenges, and studied the demand patterns across various regions along with the historical operational data to identify issues.
Forecasting Model Development
The model for predicting stock requirements was developed using ensemble learning approaches that included a mixture of machine learning and traditional statistical forecasting.
ERP Integration & Workflow Automation
The entire solution was developed in the clientโs ERP environment of Dynamics 365 and integrated with its reporting and replenishment process.
Testing & Optimization
Tests were conducted using the actual and historical data available for all the distribution centers.
Key Outcomes
$2.4M Annual Cost Savings
Validated by the clientโs finance team โ driven by reduced overstock, fewer emergency orders, and more efficient inventory management.
34% Reduction in Overstock Inventory
More accurate forecasting cut excess and slow-moving stock across all distribution centers.
61% Fewer Emergency Orders
Better replenishment visibility sharply reduced urgent supplier orders and expedited freight costs.
98.2% Product Availability Rate
Availability improved without increasing overall inventory levels.
4,000+ SKUs Managed Efficiently
Daily inventory planning across thousands of products, with no reliance on spreadsheets.
Transforming Supply Chain Planning with AI
This case study shows how effective demand forecasting helps distributors improve inventory planning without disrupting existing processes and systems. Rather than replacing the people who manage the supply chain, the system was built to support them.
As the solution matures, future phases will focus on supplier performance intelligence, automated demand sensing, and customer-driven replenishment forecasting.
Letโs build supply chain solutions that are scalable, data-driven, and engineered for operational efficiency. Contact us today.
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