Inventory reduction by multi echelon optimization

Flavors and fragrances

About the company

Our customer is the global market leader in flavors and fragrances developed for the food and beverage industry, household goods, grooming and personal care products, and perfumes with a sales volume of around CHF 6 billion with 30% market share. Their production technology is either chemical or based on extrusion from natural ingredients. The customer has a staged production process: ingredients (MTS) and compounding of finished products (mainly MTO) resulting in complex inter-company good flows and a high level of dependencies. The flavors and fragrances are custom-made and sold under confidentiality agreements.


The challenge

The company is on a journey to improve its supply chain planning capabilities. As a part of these improvements, an inventory assessment is executed. The company already runs a high-quality stock parameter setting in SAP. They asked EyeOn to further optimize inventory settings in their supply chain with >60 locations, 13 echelon levels (BoM and locations) with 200.000 products, and about 200 million records.


The project

  • Kick-off and data collection
  • Data validation & analysis
  • Validation and analysis of leadtime, supply uncertainty and demand analysis, batch sizes, FG – RM relation analysis, safety stock analysis
  • Base-case determination, identification low hanging fruit, modeling intermittent demand, classification of demand, and replenishment policy per class
  • Determining network effects of lot sizing
  • Scenario optimization
  • Set policy and determine optimal settings
  • Optimize over echelons (e.g. local and central stocking)
  • Improvement roadmap



We determined considerable inventory savings. We created data-driven insights in inventory drivers (e.g. lead time analysis), and directed quick wins and practical improvement projects. We applied state-of-the-art multi-echelon inventory models (MEIO) showing optimized parameters. The optimized parameters were implemented using a decision tree logic. We extensively trained the planners in their Center of Excellence in a multiple-day workshop to understand MEIO and the forthcoming decision tree. Confirmed savings in the first year are over 20 M€.


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