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Digital Transformation of Demand and Supply Planning in Multinational Athletic Apparel Brand
Technology Category
- Functional Applications - Inventory Management Systems
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Apparel
- Retail
Applicable Functions
- Logistics & Transportation
- Warehouse & Inventory Management
Use Cases
- Demand Planning & Forecasting
- Digital Twin
The Challenge
The multinational athletic apparel and footwear brand, with a global presence, was facing challenges due to its quickly changing marketplace, long lead times, and fragmented manual tools. These factors were hampering forecast accuracy and fill rates. The company was struggling with a manual, time-intensive demand forecasting process that was unable to keep pace with market trends or shape demand effectively. Additionally, there was a lack of effective and efficient planning of aggregated raw material purchases, resulting in unnecessarily long lead times and less agility to react to changing demand. The process of matching supply and demand was complex and time-consuming, and it didn’t maximize the ability to respond to market volatility or quickly rebalance inventory based on demand location.
About The Customer
The customer is a multinational athletic apparel and footwear brand headquartered in the US. Their product lines include shoes for Running, Basketball, Baseball, Skateboarding, Everyday, and Apparel. The company has a global presence and operates in a quickly changing marketplace. The company was using homegrown solutions and Excel for their demand and supply planning, which was resulting in long lead times and fragmented manual tools that were hampering forecast accuracy and fill rates.
The Solution
The company partnered with o9 to digitally transform their demand and supply planning. They utilized o9's ML analytics to include and analyze demand drivers and perform attribute-based planning, which improved forecast accuracy for pre- and in-season products in markets across the globe. The company also leveraged material purchasing planning, scenario modeling, and pre-build calculations from o9 to effectively collaborate with suppliers and become more agile. Furthermore, with o9’s Supply Chain Solver engine, the company created a digital twin of the network and quickly enabled agile responses to demand with inventory and capacity deployed to the right place at the right time. The o9 Enterprise Knowledge Graph was leveraged to build market, demand, and supply-knowledge models, allowing to run all key planning processes for all products and DCs, across all time horizons in a single integrated platform.
Operational Impact
Quantitative Benefit
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