Managing and developing effective inventory strategies and policies is more difficult as companies expand their operations globally, increase the number and locations of their trading partners, proliferate their product offerings and face increased demands from customers to fulfill their orders faster. These factors increase operational complexity and can result in longer lead times and more demand variability. This requires organizations to consider their inventory decisions holistically across their supply chain. Failure on this front drives increased costs in working capital and service level issues.
The llama.ai platform enables companies to analyze and properly categorize demand, factor all aspects of inventory for both existing and new supply chain structures and simulate real-world behavior. This enables a true understanding of the operational realities of your inventory strategy and policies prior to implementation. The result is a prescription for the right form, function, placement and levels of inventory. Not only will you make better, more complete inventory decisions, but you will be able to accurately monitor the associated performance indicators and adjust as needed through a highly repeatable process.
- Answer supply chain design questions considering the impact on various types of inventory such as cycle stock, safety stock, prebuild, work in process and in-transit inventory
- Build and test dependable, repeatable inventory optimization processes across scenarios
- Better understand consumer demand and service level mix to identify the right safety stock targets across all echelons of your supply chain to meet customer and company goals