Developing custom smart purchase planning algorithm to save millions for a publicly-traded flour company
Cerestar is a leading and publicly-traded wheat flour mill company in Indonesia, serving not only Indonesia but regional customers as well. With three major mills across the country, Cerestar receives daily shipments of up to 70,000 MT, produces daily on average nearly 1,000 MT of wheat, and serves both B2C and B2B clients. Given the scale of its business activities, Cerestar needs to ensure that its procurement processes are accurate, cost-effective, and efficient for the future.
Cerestar's previous procurement system was very inefficient, utilizing manual calculations of flour compositions and wheat stock outputs, forecasting, and optimization in order to make purchasing decisions. This manual process produced a lot of constraints alongside human error and inefficiencies, resulting in significant missed opportunities in cost savings.
We developed our own AI-powered smart purchase planning algorithm, powered by machine learning unique to Cerestar's business, in order to provide the best information and proposed plan of action for the Cerestar procurement team.
As part of our research and development, we developed various algorithms and mathematical formulas to reach the most optimal multi-period gristing solution. We produced our own whitepapers detailing our methodology, logic, and techniques, to ensure that the algorithm can be continuously updated and maintained to suit the ever-evolving harvest conditions.
This custom procurement optimization system featured AI-driven forecasting to predict market price trends, automated purchase planning to reduce decision-making time (from 1 week to 5 minutes), and an optimization algorithm to ensure the product's 100% compliance with established standards.
In addition to the smart purchasing algorithm, we also built custom internal tools and platforms designed to optimize communication and streamline workflows across Cerestar's diverse divisions and teams. This integration guaranteed that the efficiency gains from the algorithm were successfully extended throughout the company's entire operational ecosystem.
- annually in cost savings achieved (in USD$)
- ~$0.0M
- accuracy in procurement decision-making
- 0%
- core features implemented
- 0+
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