Inventory planning is the process of dynamically determining inventory levels and reorder parameters using the latest data on market conditions and supply chain variables while applying data analytics such as stochastic optimization and machine learning to predict inventory requirements. The benefits of AI-based inventory planning include reduced inventory levels, improved service levels, and greater flexibility to market conditions.
Traditional inventory planning methods in MRP (material requirements planning) systems rely on a set of pre-determined rules for re-ordering stock. Such methods are static and do not:
Machine learning models for AI-based inventory planning analyze variability in demand, supplier delivery times, quality issues, production disruptions, and other factors to dynamically and continually optimize reorder parameters. This helps minimize inventory holding and shipping costs while ensuring that the right part or product is at the right place at the right time.
Manufacturers often allow customers to configure hundreds of individual options, leading to products that could have thousands of permutations. Because the final configuration of a product is often not known until close to submission of the order, manufacturing companies need to have significant excess inventory on hand to be able to fulfill their orders on time. Inventory holding costs are a significant business cost, not just in capital costs but also in write-downs. In addition, when managed with static inventory planning methods supply disruptions and fluid market conditions often lead to stock-outs with lost business and poor customer experience.
C3.ai provides both a pre-built SaaS inventory planning application, C3 AI® Inventory Planning, as well as the C3 AI Suite, a complete, end-to-end platform for developing custom enterprise AI applications. The C3.ai Inventory Optimization application aggregates data from different enterprise and extraprise systems to maintain a constantly updated picture of market variability and supply chain conditions. It uses machine learning to analyze variability, learns from past data, and dynamically and continually optimizes reorder parameters for hundreds of thousands of SKUs. Some global manufacturers have used the C3 AI Inventory Optimization application to reduce inventory levels by 25 to 35 percent while improving customer service levels.
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