ยฃ2.4M Saved Annually Through AI Demand Forecasting
Transforming Supply Chain Planning with Intelligent Inventory Forecasting
Client Overview
A large distribution and logistics company in the United States partnered with Prakash Software Solutions Pvt. Ltd. to improve inventory planning across its multi-site distribution network.
The organization had more than 4,000 SKUs operating out of several distribution centers to meet the needs of its customers with fluctuating demand patterns regionally. With the increasing growth of the company, the inventory management was becoming tough. There were instances where some distribution centers maintained extra inventories for several months, whereas others had shortages regularly leading to emergency orders and delayed deliveries.
The company used spreadsheet data and historical assumptions along with manual decision-making in its forecasting and replenishment strategies. This may have been successful in the past when the operations were limited but now with the increasing complexity in the supply chain process it was proving to be challenging.
What the customer required was a forecast method that provided more clarity about the movements of inventory and enabled faster and accurate replenishment.
Industry
Distribution & Logistics
Function
Supply Chain Planning
Location
United States
Project Duration
14 Weeks
Services Provided
- Development of demand forecasting platform with AI technology
- Optimization of multi-location inventory planning
- 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 at multiple distribution centers was becoming the most challenging task for the company.
Demands were highly dependent on regional peculiarities, customer behavior, promotional activity, weather, etc. While one distribution center had excessive stock of some product category, others could have shortages in exactly the same products.
The manual forecasting system was spreadsheet-driven, based on the purchasing departmentโs experience and reactionary approach. Due to increasing quantities and complexities, balancing SKUs between different locations became very challenging for the staff.
Some of the major challenges included:
- Managing over 4,000 SKUs across multiple distribution centers
- Regional demand fluctuations and seasonal buying patterns
- Inconsistent supplier lead times impacting replenishment planning
- Excess stock and slow-moving inventory tying up capital
- Frequent emergency replenishment orders and expedited freight costs
- Limited visibility into cross-location inventory opportunities
- Manual forecasting workflows that were becoming difficult to scale
The business required an analysis process that would integrate information from various elements including inventory trends, supply chain actions, market indicators, and local site inventory movements so that they could make more informed decisions proactively.
โThe biggest challenge of the project is the balance between orders at different locations without the over-order or shortage of inventory. It was essential that the forecasting algorithms we used take into account the daily processes of the company and not only the historical pattern of sales.
Close collaboration was maintained with logistics planning managers and operational management during the project in order to provide a set of feasible, understandable recommendations that would allow for reducing extra inventory expenses.โ
Manish Langa
AI Practice Head, PSSPL
How PSSPL Helped
An intelligent demand forecast and replenishment system has been designed by our team according to the business processes of the client as well as his supply chain.
The system made use of three years’ sales history, along with information about inventory levels, lead times from suppliers, customer demand trends, promotional schedules, and external demand indicators to recommend replenishments at SKU level.
This technology was designed in a way that could work in tandem with the current Dynamics 365 system used by the customer, thus eliminating the need for infrastructure overhaul and ERP replacement.
Predictive analysis and operation visibility allowed for decreased wastage, better balance of inventories, and increased confidence in replenishment decisions made by supply chain teams.
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
Lorem We build an deep learning models for industries aligned with an analysis of their needs. Each solution is built to reduce manual effort, improve customer experience, scale, stay compliant, and integrate seamlessly with existing systems.
Discovery & Supply Chain Assessment
We worked with procurement, inventory planning, and operations departments to gain insights on their replenishment process flows, demand patterns in regions, supplier performance, and inventory choke points. We conducted historical analysis of the operational data to uncover any gaps in forecasting and inefficiencies.
Forecasting Model Development
We have built ensemble forecasting solutions using machine learning algorithms and statistical forecast modeling approaches to increase the accuracy of predictions.
ERP Integration & Workflow Automation
The solution has been deployed in the clientโs Dynamics 365 ecosystem along with their inventory planning, reporting, and replenishment processes.
Testing & Optimization
A lot of tests were conducted based on historical as well as operational data from all distribution centers. The forecasting methodology and replenishment decisions were consistently improved on the basis of feedback and results.
Key Outcomes
ยฃ2.4M Annual Cost Savings
The company made annual savings that were validated by the finance department through reduced inventory overstock, emergency orders, and efficient inventory management.
34% Reduction in Overstock Inventory
Accuracy in forecasting helped decrease excessive inventory and low-movement items from all distribution centers.
61% Fewer Emergency Orders
Visibility in replenishment led to a drastic reduction in urgent orders from suppliers as well as emergency transportation charges.
98.2% Product Availability Rate
Product availability was enhanced within the companyโs operations without having to enhance inventory levels in aggregate terms.
4,000+ SKUs Managed Efficiently
The forecasting system assisted in the daily inventory planning for the thousands of products in question without relying on a spreadsheet.
Transforming Supply Chain Planning with AI
This case study illustrates how effective demand forecasting can assist distributors in improving their inventory planning without interfering with the current processes and systems. Instead of trying to replace the people managing the supply chains, the system was used to facilitate them in doing so.
As the solution evolves, 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 designed for operational efficiency.
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