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Case Studies > Unilever's Demand Sensing and Inventory Optimisation with Terra Technology

Unilever's Demand Sensing and Inventory Optimisation with Terra Technology

Technology Category
  • Analytics & Modeling - Predictive Analytics
  • Application Infrastructure & Middleware - Data Exchange & Integration
  • Functional Applications - Inventory Management Systems
Applicable Industries
  • Consumer Goods
  • Retail
Use Cases
  • Inventory Management
  • Predictive Maintenance
  • Supply Chain Visibility
Services
  • Software Design & Engineering Services
  • System Integration
The Challenge
Unilever faced significant challenges in managing its supply chain due to increasing volatility in the market. The company identified five key global trends impacting its operations: multiple channels, sustainability, economic volatility, customer intimacy, and digital savviness. To address these challenges, Unilever needed a more agile supply chain that could handle the growing volatility without resorting to expensive inventory increases. The company aimed to improve its short-term forecast accuracy and reduce working capital tied up in inventory.
About The Customer
Unilever is one of the largest FMCG manufacturers globally, with a presence in over 190 countries and a portfolio of more than 400 brands. The company serves 2 billion consumers daily and primarily uses SAP systems for its operations. Unilever's demand planning is maintained within SAP Advanced Planner and Optimizer (APO). The company has a significant global reach and is committed to sustainability and innovation in its supply chain processes.
The Solution
Unilever partnered with Terra Technology to implement demand sensing and inventory optimisation tools. Terra Technology's Enterprise Demand Sensing Platform automates and synchronises various demand signals to improve short-term forecast accuracy. The Multi-Enterprise Inventory Optimisation platform provides optimal inventory targets across the supply chain, balancing cost and service while minimising waste. The implementation required senior management buy-in and a dedicated project team. The roll-out was phased, starting with more mature areas and gradually building confidence in the system's outputs.
Operational Impact
  • Unilever realised significant improvements in forecast accuracy, reducing error and bias within its critical short-term horizon.
  • The company was able to reduce safety stock levels over time, leading to a 35% reduction in inventory in Europe.
  • Improved forecast accuracy and inventory reduction contributed to cost and waste reductions, aligning with Unilever's sustainability goals.
Quantitative Benefit
  • MAPE improved by 22% on a seven-day horizon through the use of Demand Sensing.
  • Unilever was able to cut inventory in Europe by 35%.

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