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Optimize safety stock

Safety stock management and its efficiency directly impacts supply chain performance, customer experience, and turnover KPIs. Our solutions for demand forecasting quantify uncertainties, account for underbuy and overbuy costs, and find optimal inventory levels that reduce costs and maximize profits. These capabilities create a solid foundation for safety stock risk management and help to improve the effectiveness of supply chain management for consumer goods, which is greatly influenced by the levels of safety stock.

Optimize ship-from-store inventory

Ship-from-store and buy-online-pickup-in-store use cases have unique challenges related to serving both digital and physical channels from the same inventory pools. Our inventory optimization software provides unique capabilities for balancing order pick rates, online availability rates, inventory replenishment service level agreements (SLAs), and fulfillment risks in real time. This helps to optimize inventory turnover and supply chain costs without compromising on the omnichannel strategy. These features help with demand planning and decision making to ensure your supply chain network is operating at maximum efficiency.

Optimize in-store inventory movement

Our inventory optimization solutions for retailers include smart tools for in-store inventory movement. These tools use predictive analytics to estimate the optimal amount of merchandise that needs to be moved from the back room to front room to avoid out of stock situations. Mobile decision support apps substantially improve customer experience and the efficiency of store operations. If you can properly manage inventory movement, then no items in your store should be out of stock and your customer will always be able to purchase from you.

Use intelligence tools for sourcing and distribution

Managing dozens of suppliers and distribution centers is a tough problem that requires advanced decision support tools. We build intelligent tools that use machine learning and optimization algorithms to identify the most efficient sourcing and logistics options in real time and suggest them to business users. These tools help to improve the quality of decisions and speed up the supply chain optimization process. Our supply chain management software is critical for optimizing your supply chain.

Optimize order sourcing

Optimal order placement in an environment with multiple distribution centers and stores is a complex problem that requires near real-time combinatorial optimization. Our order sourcing software can find optimal placement schemas that minimize transportation costs and delivery times for consumer goods.

Optimize multi-echelon inventory movement

Our supply chain optimization software uses multi-step optimization algorithms, demand forecasting, and stochastic simulations to improve production planning and availability SLAs and to reduce transportation costs and inventory costs in multi-echelon environments. Our supply chain management software can really help ensure that your strategic planning is done efficiently and effectively to promote growth.
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
Retail
We help retailers to streamline inventory management processes in warehouses and in stores. We offer unique solutions for retail such as optimization for buy-online-pickup-in-store use cases.
Manufacturing and CPG
We build demand forecasting and inventory optimization models for manufacturers and CPG companies to help them optimize both the supply and distribution chains.
Healthcare
Supply chain optimization can improve hospital margins, and we help healthcare companies to accomplish this using our extensive experience in a variety of industries.
Safety stock optimization for ship-from-store
read moreIn 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.

Safety stock optimization for ship-from-store

read moreIn 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.
read moreIn 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.
Deep reinforcement learning for supply chain and price optimization
read moreThis 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.

Deep reinforcement learning for supply chain and price optimization

read moreThis 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.
read moreThis 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.
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.
read moreIn 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.

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.
read moreIn 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.
  • 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.
read moreIn 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.

We provide flexible engagement options to design and build a supply chain optimization solution for your company. Contact us today to start with a workshop, discovery, or proof-of-concept (POC).

Workshop
We offer free half-day workshops with our top experts in supply chain analytics and optimization technologies to discuss your supply chain management strategy, challenges, optimization opportunities, and industry best practices.
Proof-of-Concept
If you have already identified a specific use case that needs to be solved, we usually can start with a 4–8-week proof-of-concept project to deliver tangible results and business value.
Discovery
If you are in the stage of requirements analysis and strategy development, we can start with a 2–3-week discovery phase to identify the correct use cases for supply chain analytics and optimization, design your solution using industry best practices, and build an implementation roadmap.
Stream processing
Marketing spend optimization
Fraud detection & prevention
Customer intelligence and personalization
Trade promotion optimization
Revenue and price optimization
Anomaly detection and predictive maintenance

Want to get in touch with us? We are pleased to begin helping you.

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