Supply chain optimization

Optimize hundreds of supply chain and pricing parameters using artificial intelligence, predictive models, and simulations. You can simplify demand planning and inventory planning and reduce lead times and stock-outs using our software solutions that optimize your supply chain processes and decisions.

Our clients

Finance & Insurance

How our supply chain optimization technologies work

How our supply chain optimization technologies work
Demand forecasting
We use advanced machine learning techniques to accurately predict demand, taking into account product properties, marketing events, and market-wide signals. Accurate demand forecasting requires clean and comprehensive data; therefore, we put considerable emphasis on data consolidation and quality to achieve superior results.
Break-even analysis
Our supply chain solutions account for stock-outs, inventory turns, and holding costs to properly balance underbuy and overbuy risks. This helps to implement a sound, cost-effective risk management strategy. Our supply chain management software is ready for when you decide to implement it next.
Strategic optimization and simulations
Our supply chain and inventory management solutions use the latest optimization technologies such as reinforcement learning (RL) to identify and account for demand patterns and uncertainties, analyze billions of pricing and inventory management scenarios, and find optimal, cost-effective solutions. The management systems we can put in place, using the latest business intelligence techniques, can enhance the overall profitability of your company.
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We develop supply chain optimization software for enterprises from many industries including retail, manufacturing, and healthcare

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Safety stock optimization for ship-from-store
In this article, we describe the inventory optimization problem for buy-online-pickup-in-store and ship-from-store use cases and provide a case study that shows how stock levels can be optimized in real-life settings using machine learning methods.
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Deep reinforcement learning for supply chain and price optimization
This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.
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Multi-agent deep reinforcement learning for multi-echelon supply chain optimization
In this article, we explore how the supply chain optimization problem can be approached from the RL perspective that generally allows for replacing a handcrafted optimization model with a generic learning algorithm paired with a stochastic supply network simulator. We start by building a simple simulation environment that includes suppliers, factories, warehouses, and retailers, as depicted in the animation below; we then develop a deep RL model that learns how to optimize inventory and pricing decisions.
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