Today’s supply chains move at a ferocious pace fueled by multiple data streams from both internal and external enterprise systems, social networks, syndicated streams, Internet of Things (IoT) and more.
Advances in machine learning help transform this data to better predict customer needs, identify trends and deliver a more synchronized supply chain from product concept to customer availability. Inventory Optimization (IO) can have a huge financial impact by freeing up working capital while boosting service and minimizing inventory. Harnessing the insights of multiple data streams, Inventory Optimization determines where and how much stock to hold to meet a designated service level while complying with specific inventory policies. Through sophisticated machine learning algorithms, IO makes stocking recommendations to satisfy these needs.
Multi-echelon Inventory Optimization (MEIO) goes a step further to optimize stock locations and amounts across all sites and nodes in a supply chain network. The right MEIO approach automates the stocking and replenishment process as well as enables rich scenario analysis to automatically analyze tradeoffs between costs and service levels. It also uses machine learning to identify stocking patterns for seasonal products or new product introductions. Through robust visualizations, MEIO dashboards and event driven notifications help improve usability, user adoption and user efficiency.
This Advanced Inventory Optimization Handbook more deeply explains the importance of IO and MEIO strategies to help minimize costs and reduce risk while meeting customer service requirements, and provides examples of how to build these capabilities at your company, including a handy checklist if your organization already has an inventory optimization initiative underway.