Optimizing Raw Materials Cost using Knowledge Graph enabled AI
Raw materials cost is the largest variable manufacturing cost and consumes most of the revenue.
Low Cost Recipe
In coating industry, coating films are made of various types of raw materials like Polyethylene, Polypropylene, Resin and many more. Every customer comes with his own quality requirements depending on the end application of the film. This requires coating manufacturer to find the best combination of raw materials mix from nearly infinite combinations. Also, meeting the individual customer demand at least cost possible and within a fixed timeframe is key to be successful in coating business.
Top Management wanted
Our client, who is leader in the films industry, already has a wide rage of products around 800 types of films products with 4-5 variants in film thickness to meet specific customers demand. But their top management knew they were leaving money on the table in terms of high raw materials cost. Also, they didn’t want to loose a customer who was in hurry to get specialty film which was not present in their product line yet.
The MatSci Solution
Materials Costimizer utilized recipe data of 800 products to automatically build a knowledge graph to find correlation between various types of polymers, epoxy resins, film properties and process parameters.
Further, AI modules were trained as per knowledge graph embeddings and all existing products recipe were optimized as per their indirect and direct cost.
Materials Costimizer provided low cost alternatives for existing film products and most economical recipe possible for new target product specifications within a second.
Saves around $2.9M of raw materials cost per year
Finds cheapest product recipe within 5 seconds
Increases profitability by 20%
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