How AI is Optimizing Inventory Management

Efficient inventory management is critical to the profitability of manufacturing companies. However, determining optimal inventory levels is often a complex task with many variables to consider including demand, order lead times, supply chain constraints, and holding costs.

Artificial intelligence (AI) offers new potential to tackle this challenge. Using machine learning techniques, AI systems can analyze historical data and detect intricate patterns to more accurately forecast future demand.

These forecasts allow the AI to optimize stocking policies and reorder points for each product, accounting for all relevant constraints and expenses. Ultimately, companies can reduce inventory carrying costs while maintaining the necessary level of customer service.

In addition to automating demand prediction and inventory optimization, AI can also contribute significantly to other aspects of the supply chain. For instance, by assessing supplier risks, planning transportation and routing, scheduling warehouse operations, and coordinating returns logistics.

HESH’s AI-Driven Inventory Management R&D

HESH is working on integrating AI capabilities into our next-generation manufacturing execution platform. We are developing specialized AI modules for demand forecasting, inventory optimization, and production planning.

This will enable our clients to achieve greater forecast accuracy, lower warehouse costs, and avoid stockouts. Leveraging HESH’s flexible interfaces, our inventory management AI will be fully integrated with resource planning systems, accounting, and other applications in our platform.

We are applying cutting edge machine learning techniques like LSTM networks and backpropagation neural nets to develop these AI components. This will allow HESH to deliver continuous supply chain and manufacturing optimization for our customers.