Case Studies Optimized Fulfillment For Market Basket Analysis
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Optimized Fulfillment For Market Basket Analysis

Analytics & Modeling - Predictive Analytics
Functional Applications - Inventory Management Systems
Functional Applications - Warehouse Management Systems (WMS)
Consumer Goods
Retail
Logistics & Transportation
Warehouse & Inventory Management
Inventory Management
Predictive Replenishment
Supply Chain Visibility
Data Science Services
System Integration
A leader in novelty gifts production and distribution was struggling to fulfill orders on-time while experiencing a higher amount of split shipments that increased their shipping costs. Due to a lack of visibility of available products in different warehouses, each warehouse had the wrong combination of SKUs and volumes of products.
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The customer is a leading company in the production and distribution of novelty gifts. They have a large-scale operation with multiple warehouses distributed across various locations. The company has been facing significant challenges in managing their inventory and fulfilling orders on time. The lack of visibility into their inventory levels and the incorrect combination of SKUs in different warehouses have led to increased shipping costs and a higher number of split shipments. This has not only affected their operational efficiency but also their customer satisfaction levels. The company was in dire need of a solution that could provide real-time visibility into their inventory and help them optimize their fulfillment processes.
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ConverSight analyzed 24 months of demand to deliver market basket analysis and identify commonly sold goods by consumer & location. They provided real-time visibility into aging inventory levels of all products for optimized inventory management. Additionally, ConverSight produced key metrics and proactive insights on inventory for reduced inventory costs. This comprehensive approach allowed the company to better understand consumer purchasing patterns and accurately forecast product levels. By consolidating products across distributed warehouses, they were able to reduce shipments and improve on-time delivery.
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Significant improvement in delivery times and reduction in shipment costs.
Visibility into buyer demand and trends to accurately forecast product levels.
Better understanding of consumer purchasing patterns to drive sales and promotion.
22% Increase in on-time deliveries.
17% Reduction in split shipments.
25% Decrease in monthly shipment costs.
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