1 Inventory Planning Optimization-Inventory Optimization
This guide describes the use of Inventory Planning Optimization-Inventory Optimization (IPO-IO). For details about the implementation of IPO-IO, see Oracle AI Foundation Cloud Services Implementation Guide.
Inventory Planning Optimization-Inventory Optimization (IPO-IO) determines the optimal time-phased replenishment plan that consists of the replenishment policies, that is, the reorder point (RP) and receive up-to level (RUTL), and the recommended order quantity for PO and transfers at item/location/day level for the configurable planning horizon. The optimal plan is generated using simulation and optimization methods and considers inputs such as supply chain network, replenishment attributes, and business rules. Moreover, the optimization engine uses machine learning methods and simulation-based optimization to calculate the trade-offs between the service level and the inventory cost for different replenishment policies. The trade-off analysis is leveraged to generate the optimal replenishment policies for achieving a desired target service level. In addition to the replenishment plan, IPO-IO recommends optimal rebalancing transfers between stores to increase sell-through and to avoid markdowns. This type of recommendation can be turned off when not applicable (for example, for grocery categories).
The data-driven replenishment policies, PO/transfers, and rebalancing transfers are pushed to Oracle Retail Merchandising System (RMS) to execute purchase orders and transfers. Optionally, the retailer can choose to send only the replenishment policies to RMS and have the final order quantity and PO/transfers be calculated and generated in RMS.
IPO-IO leverages historical sales, inventory positions, replenishment attributes such as lead time and review schedule, business requirements such asstore priorities for shortfall reconciliation, and the demand forecast to generate the optimal time-phased replenishment plan. The demand forecast that is generated by the forecast engine within AI Foundation considers different factors such as price effect, holidays, and promotions, and variation across customer segments.
User roles are used to set up application user accounts through Oracle Identity Management (OIM). See Oracle Retail AI Foundation Cloud Services Administration Guide for details.
User roles are used to set up application user accounts through Oracle Identity Management (OIM). See Oracle Retail Advanced Science Cloud Services Administration Guide for details. User must have the following roles assigned.
- ADMINISTRATOR_JOB in order to access Control & Tactical Center.
- INVENTORY_ANALYST_JOB in order to access IPO-IO application.
- Access to Innovation Workbench and/or Data Visualizer. This is necessary in order to query or visualize the data and verify that the data loaded matches the desired expectations.
- Access to POM to execute ad hoc and batch jobs. The POM UI url is something like <host>/POMJetUI. If the user cannot access the POM UI, contact the administrator to obtain the relevant access/user roles.