1 Inventory Planning Optimization

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1 Inventory Planning Optimization

This guide describes the use of Inventory Planning Optimization (IPO). For details about the implementation of IPO, see Oracle AI Foundation Cloud Services Implementation Guide .

Inventory Planning Optimization (IPO) 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 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 leverages historical sales, inventory positions, replenishment attributes such as lead time and review schedule, business requirements such as store 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 application.

In addition to above roles, verify the following:

• 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 /POMJetUI. If the user cannot access the POM UI, contact the administrator to obtain the relevant access/user roles.

What is a Run

A Run is the execution of a set of system calculations to generate recommendations based on the latest available inputs.

IPO supports two kinds of runs: user runs and batch runs. Batch runs are scheduled to run automatically at regular intervals (for example, weekly or daily). By default, the batch runs are scheduled for daily execution. User runs are typically created by the user to do what-if analysis and/or to override the recommendations generated by the latest batch run. IPO runs generate recommendations at the item-location level. The items that are included in a run can be selected by specifying one, a multiple, or all of the nodes at a higher merchandise level, such as department. This level is configurable. Similarly, the locations that are to be included in a run can be selected by specifying one, a multiple, or all the nodes at a higher location level, such as area. This level is configurable.

Output of IPO Run Results

IPO runs generate one or multiple type of recommendations, as described below. The type of analysis to be done in a run is selected in the UI (for ad-hoc runs) or determined by configurable flags (for batch runs).

Time-Phased Planning

Time-phased planning is the main type of analysis that must run as part of the batch, so the replenishment policies and PO/transfers are generated daily. The time-phased planning optimizes the distribution of inventory through the life cycle for every SKU and throughout the entire supply chain network. It provides point-in-time and time-phased purchase orders and transfers (warehouse-to-store and warehouse-to-warehouse),

The number of item-locations that are processed in each run can vary, based on the retailer’s review schedule. During each run, only the item-locations that are due for review for replenishment on the next day are processed. For example, a run that is scheduled for a nightly batch on Sunday 01/08/2024 would generate optimal replenishment plan for itemlocations that have a next review date of Monday 01/09/2024 in the retailer’s replenishment system. This allows the end user to review and approve the policies and/or PO/transfers that were not auto-approved on Monday before they are pushed to the replenishment system for generating and executing orders.

The replenishment policies are characterized by two parameters: re-order point (RP) and receive up-to level (RUTL). These parameters are optimized and generated at the sku-location and can be reviewed and approved by the user or may be auto-approved. The auto/user approved policies can be exported to the MFCS to generate and execute transfers and purchase orders. If the user approves the recommended PO/transfers within IPO, the approved orders can be exported to the MFCS. There are separate jobs for the export of replenishment policies, PO, and transfers so they can be enabled based on the business requirements.

PO/Transfers are executable because delivery policies, delivery schedules, company closures, location closures, and exceptions are applied to the ordering and delivery days. Closures are short term closures such as holidays. This foundation data identifies the type of closure, shipping and/or receiving.

When using delivery schedules, the lead times represent the minimum amount of time in which an order could be delivered to that location. Replenishment then dynamically calculates the lead time of the review cycle by taking the minimum lead time and adding an additional number

of days to reach the next valid delivery day. If a delivery schedule is not entered, every day is considered as a valid delivery day.

Note

Truck scaling is executed by default after the time-phased planning run finishes. There is no option to enable/disable the truck scaling process.

Trade-Off Analysis

These curves show the trade-off between metrics such as inventory cost and sales revenue for different target service levels. Trade-off curves are generated at the sku-location as well as the aggregate level of merchandise and location.

The trade-off analysis is not the kind of process that must be run daily because the changes in sales patterns, replenishment rules, and supply chain network are not that frequent. Therefore, it is typically recommended that the optimal trade-off curves updated quarterly or when there is a major change in one of the factors mentioned above.

Rebalancing Transfers

Rebalancing transfers are the transfers between stores. The goal of rebalancing transfers is to shift unproductive inventory and place it in locations that have a higher likelihood of selling and with a better margin, while minimizing the total cost of transfers (for example, shipping costs and any up-charges). Rebalancing transfers are generated at the sku-origin-destination level. The user can review the recommended rebalancing transfers and approve them. The approved transfers are pushed to the retailer’s execution system.

Workflow

The IPO workflow starts with the completion of a scheduled run or by the manual creation of a new run. Once a run has completed, you can view the recommendations of the run or the timephased inventory plan..

Your core primary tasks are:

• Reviewing optimized replenishment policies that have not met automatic approval rules.

• Analyze Alerts to determine if order adjustments are required for today’s orders. Then future inventory issues are analyzed to determine necessary policy adjustments.

• Review and approve orders including rebalancing transfers.

Use the Inventory Planning Alerts view, Forecast Overview and Inventory Overview to support your analysis of the recommendations.

The IPO UI workflow consists of the following:

• Overview: This is the dashboard for the IPO runs. In this tab, the user can see a list of all existing runs, along with details that describe each run.

From the run overview table, the user can create a run, copy a run, open a run, or delete a run. When the user clicks on a successful run, it is opened in a new tab with three sub-tabs to show replenishment policies, rebalancing transfers, and PO and transfers, as well as a sub-tab to show the summary of inputs of the run. For a failed run, only the input summary sub-tab is displayed. To create a run, the user must specify the run scope, that is, the merchandise and location nodes for the run and the type of analysis to be performed.

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The following columns are displayed in the Replenishment Policies tab. For a description of many of these columns, see Glossary of Inventory Optimization Terms

• SKU Name

• Location

• Loc. Type

• Replenishment Method

• Presentation Stock

• Demo Stock

• Safety Stock

• RP

• RUTL

• FCST LT

• Fcst. Coverage Per.

• Coverage Per. Start DT

• Coverage Per. End DT

• Lead Time

• Review Time

• Target Service Level

• Primary Source

• Actual Sales Units W-1

• Actual Sales Units W-2

• Actual Sales Units W-3

• Actual Sales Units W-4

• Fcst. Sales Units W+0

• Fcst. Sales Units W+1

• Fcst. Sales Units W+2

• Fcst. Sales Units W+3

• Approved On

• Approved By

• Run

• Calculation Date

Rebalancing Transfers Tab

The Rebalancing Transfers are transfers between stores You can use the Table View and the Map View buttons to change the presentation in this tab. The Map View and Table View are explained in the Recommendations Section and shown in Figures 12.9 and 12–10.

The following columns are displayed in the Rebalancing Transfers tab. For a description of many of these columns, see Glossary of Inventory Optimization Terms

• Recommendation ID

• Date

• Origin

• Destination

• SKU

• Color

• User Quantity

• System Quantity

• • Shipping Cost

• Forecast Sales Units at Destination

• Fcst. Deficit at Destination

• On-Hand Quantity at Destination

• In-Transit Quantity at Destination

• On-Order Quantity at Destination

• Back-Order Quantity at Destination

• Holdback Stock at Dest

• Forecast Sales Units at Origin

• Forecast Surplus Units at Origin

• On-Hand Quantity at Origin

• In-Transit Quantity at Origin

• On-Order Quantity at Origin

• Back-Order Quantity at Origin

• Holdback Stock at Origin

• Approved On

• Approved By

PO/Transfers Tab

The purchase orders and transfers are the recommended purchase orders and transfers that are generated based on the optimal replenishment policies, inventory positions, and primary and secondary sources of replenishment for the store and other factors.

The following columns are displayed in the PO/Transfers tab. For a description of many of these columns, see Glossary of Inventory Optimization Terms

• Origin

• Destination

• SKU

• System Order Qty

• Truck Scale Qty

• User Order Qty

• Submitted Order Qty

• Unconst, Need

• Presentation Stock

• Demo Stock

• Safety Stock

• • RP

• RUTL

• Fcst. LT (fcst_lt)

• • Fcst. Coverage Per. (fcst_rt_lt) • Coverage Per. Start DT (fcst_rt_lt_start_dt)

• Coverage Per. End DT (fcst_rt_lt_end_dt)

• Lead Time

• Review Time

• Target Service Level

• Actual Sales Units W-4

• Actual Sales Units W-3

• Actual Sales Units W-2

• Actual Sales Units W-1

• Fcst. Sales Units W+0

• Fcst. Sales Units W+1

• Fcst. Sales Units W+2

• Fcst. Sales Units W+3

• OH Qty at Dest.

• In-transit Qty at Dest

• • On-order Qty at Dest • Back-order Qty at Dest • Holdback Stock at Dest

• OH Qty at Origin

• • In-transit Qty at Origin

• On-order Qty at Origin

• Back-order Qty at Origin

• Holdback Stock at Origin

• Date

• Expected Delivery Date

• Activate Date

• Deactivate Date

• Approved On

• Approved By

• Run Name

ORACLE a

YFittersFitters | Fiiterby Order Type: Order Type: Type: | Transter | Purchase Order Order | Allocation HB & © 3896 Tsf parents Tsf parents parents 54388 Tsflineitems Tsflineitems 2483 Ready for Review Ready for Review for Review Review Auto-approved 1400 User-approved User-approved 9 Exportedto FCS Exportedto FCS FCS 4 Exportedto MFCS Failed Exportedto MFCS Failed MFCS Failed Failed OWH/WHtransfers_ 3896 WH/Store transfers WH/Store transfers transfers ed WHywe00 aty 276,835WH/StoreWH/Store Qty Tet276,835 aty276,835 aty aty = | || || Actors 4 oe © | EO EO OWL teak | > teem A) Bmttoeeres Order No No Order Date Date Source PONo. PONo. Source Source Type Type Department Destination Destination Type Type Capacity Alert Alert SKU Count Count 2793498 05/30/23 99002-US Virtual WH Virtual WH WH East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2058-US - Madison Madison Store No 6 2793496 05/14/23 99002-US Virtual WH - East Coast DC B&M Virtual WH - East Coast DC B&M WH - East Coast DC B&M - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2123-US - Louisvile - Louisvile Louisvile Store No 6 2793495 05/21/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2126-US - Montgomery Montgomery Store No 7 2793494 06/02/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 1153-US - West Town Mall West Town Mall Town Mall Mall Store No 5 2793493 06/01/23 99002-US Virtual WH - East Coast DC B&M Virtual WH - East Coast DC B&M WH - East Coast DC B&M - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 1120-US - Quarry Quarry Store No 3 2793490 06/02/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2172-US - Allegheny - Allegheny Allegheny Store No 3 2793483 05/27/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2186-US - Augusta Augusta Store No 3 2793107 05/31/23 99002-US Virtual WH - East Coast DC B&M Virtual WH - East Coast DC B&M WH - East Coast DC B&M - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 1183-US - The Shops At Canal Place The Shops At Canal Place Shops At Canal Place At Canal Place Canal Place Place Store No 1 2793104 06/04/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2083-US - Albany - Albany Albany Store No 1 2793101 06/04/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2178-US - Daytona Beach Daytona Beach Beach Store No 4 2793100 05/18/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 1143-US - International International Plaza Store No 8 2793098 05/20/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2082-US - Providence - Providence Providence Store No ” 2793095 05/22/28 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2156-US - Baxter - Baxter Baxter Store No 9 2793093 05/14/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 1102-US - Easton Town Center Easton Town Center Town Center Center Store No 3 2793091 05/16/23 99002-US Virtual WH Virtual WH WH - East Coast DCB&M East Coast DCB&M Coast DCB&M DCB&M Warehouse 2120-US - Shreveport Shreveport Store No 8 279309027930892793089 05/16/2305/15/2305/15/23 99002-US99002-US VirtualVirtual WHWH — EastEast Coast Coast DCDC B&M B&M99002-US VirtualVirtual WHWH — EastEast Coast Coast DCDC B&M B&M VirtualVirtual WHWH — EastEast Coast Coast DCDC B&M B&MVirtual WHWH — EastEast Coast Coast DCDC B&M B&M WHWH — EastEast Coast Coast DCDC B&M B&MWH — EastEast Coast Coast DCDC B&M B&M — EastEast Coast Coast DCDC B&M B&M- EastEast Coast Coast DCDC B&M B&M EastEast Coast Coast DCDC B&M B&MEast Coast Coast DCDC B&M B&M Coast Coast DCDC B&M B&M Coast DCDC B&M B&M DCDC B&M B&MDC B&M B&M B&M B&M B&M WarehouseWarehouseWarehouse 2192-US2074-US — Bangor Pittsburgh2074-US — Bangor Pittsburgh — Bangor Pittsburgh- Bangor Pittsburgh Bangor Pittsburgh Pittsburgh StoreStoreStore NoNoNo = | 2793087 05/16/23 99002-US Virtual WH Virtual WH WH - East Coast DC B&M East Coast DC B&M Coast DC B&M DC B&M B&M Warehouse 2079-US - Syracuse - Syracuse Syracuse Store No 2

Note

In all screens that show a contextual area for the selected row of a table, you can use the icon at the top right corner of the contextual area to switch between the graph view and the single recommendation view.

Validation Exception

When the user can override the quantities and approves Po/transfers, it is possible that the overridden quantities result in an over-stock (sending more than required to a destination), under-stock (sending less than required to a destination), or under-supplied (pulling more than available from a warehouse). These exceptions are shown in the Validation Exception tab. The user can disregard the exceptions so that they no longer appear in the table or can revert the quantity to the system quantity for all recommendations that are tied to an exception.

Manual Allocation

Manual Allocation provides the inventory planner with a powerful and flexible tool to create, manage, and approve detailed distribution plans for specific products. This feature allows you to strategically allocate inventory from various sources—such as Purchase Orders (POs) and available warehouse stock—to a targeted set of destination stores and warehouses.

The entire process is designed to give you full control over how inventory is deployed, enabling you to support key business initiatives like new product introductions, promotions, or seasonal pushes.

The Manual Allocation Workflow

The workflow for creating and managing a manual allocation follows a clear, logical progression:

1. Select Inventory: Begin by identifying and selecting available inventory from the Inventory Dashboard.

2. Define Parameters: Use a guided, multi-step wizard to define the rules of the allocation, including destinations and calculation policies.

3. Calculate & Review: Trigger the system to calculate the recommended quantities based on your rules.

4. Adjust & Finalize: Review the system’s recommendations, make manual overrides as needed, and save the allocation as a “Worksheet” (draft).

5. Approve & Export: Formally approve the allocation to lock it from further edits and automatically send it to downstream execution systems like MFCS.

The Inventory Dashboard

The starting point for all manual allocations is the Inventory Dashboard . This screen provides a consolidated view of all inventory that is currently available to be allocated.

Key features of the dashboard include:

• Unified Inventory View: A central table displays all allocatable items, clearly identifying their source as a PO or Warehouse Stock in the Document Type column.

• Filtering: You can use powerful filters for Department, Class, and Subclass to quickly find the specific items you need to allocate.

• Status Tabs: Tabs across the top of the table allow you to view allocations based on their current status:

  • Available: Items that have not yet been included in an allocation.

  • Ready for Review: Allocations that have been saved as a “Worksheet” but are not yet approved.

  • Approved: Allocations that have been finalized and are awaiting export.

  • Exported / Export Failed: The final status of an allocation after the system has attempted to send it to the execution system.

Creating a New Manual Allocation

To create a new manual allocation, you will use the four-step “train stop” wizard.

1. Name and Destinations: This is the first step where you define the high-level details of your allocation.

• Allocation Information: Provide an Allocation Name for easy identification and select a Release Date .

• Destinations: Use a variety of selectors—such as Location Lists, Location Traits, or individual Stores and Warehouses—to build the list of destinations for your allocation.

When creating an allocation that will pass through multiple cross-docked warehouses, select only the final destinations, such as stores. For cross-docked item/locs, the chained allocations between all tiers will be automatically generated while you manage the initial warehouse source to final destination quantities.

2. Options: This optional step allows you to set quantity limits for specific groups of locations.

• Min/Max Limits: Define minimum and maximum quantities at the size level (for example, Small, Medium, Large) to ensure destinations receive a balanced assortment.

3. Policies: This is the most critical step, where you define the logic for the system’s calculation.

• Gross Need Parameters: Select a Demand Source (for example, Forecast, Sales Plan) and a Date Range to determine the need at each destination.

• Inventory Parameters: Specify which inventory buckets (for example, On Hand, On Order) should be included or excluded from the calculation.

• Calculation Parameters: Define whether to allocate to the “Net Need” or “Gross Need” and whether the allocation should be “Exact” or “Proportional”:

  • Net need is the Gross need reduced according to the included inventory and onhand. Gross need is not reduced.

  • Allocating to exact need gives out inventory to meet appropriately rounded need. Allocating proportionally will push all of the source inventory even if the destinations get more than need.

4. Review and Save: This final step provides a summary of all the parameters you have defined. After reviewing, you can click Calculate Allocation to trigger the system calculation. The allocation will then be saved in a “Worksheet” status.

Editing and Approving an Allocation

Once an allocation has been calculated and saved as a “Worksheet”, you can open it at any time to review, edit, and approve it.

Viewing and Editing Quantities

The View/Edit Allocation Detail screen displays the results of the system’s calculation. From here, you can:

• Review Calculated Quantities: Analyze the system-recommended Allocated Qty for each item/destination.

• Manually Adjust Quantities: Enter a new value in the Manual Qty column to override the system’s recommendation. This gives you precise control over the final distribution.

Approving the Allocation

When you are satisfied with the final quantities:

1. Click the Edit icon in the header to open the details panel.

2. Change the Allocation Status from Worksheet to Approved .

3. Click the Save button.

This final action will lock the allocation from further edits and automatically trigger its export to your merchandising and warehouse management systems for execution.

Plan View

The retailer’s most significant investment is the retailer’s inventory. Inventory Planning Optimization Cloud Service (IPO) offers the retailer the ability to best predict how much demand there will be and to deploy the inventory to optimize the demand throughout the course of an item’s life cycle. Throughout an item’s life cycle, the application reacts to changes in consumer behavior in order to right size inventory deployment and demand methodologies.

The end result allows the retailer to manage the current and future inventory at scale to ensure the right products and quantiles are in the right place for the right customers at the right time, in an automated and intelligent way, through the user interface.

The Inventory Plan View allows you to view a projection of inventory and order recommendations during a specific time frame.

Use the main plan view to perform the major part of your analysis. This view helps to break down the components of the inventory and provides insights into the calculated target stock levels (Order Point (OP) and Order Up to Level) that drive ordering. In this view you see receipts increasing inventory but also see the order recommendations on the order date. These order recommendations create demand on the source that can be viewed in the warehouse demand or supplier order forecast, depending on the location’s source.

To view the time-phased plan, complete the following steps:

1. Enter your time frame (see Time Frame).

2. Define the products and locations using the filter (see Main Filter).

3. Click Apply on the Main Filter to update the Plan View based on your choices.

4. Use the Cycle area arrows in the top right to navigate through the pages (see Cycle Area).

Use the Purchase Orders sub-filter to select purchase orders whose products and locations will be included on the Main filter.

• If a purchase order has been selected, and then additional products or locations are selected, then a Resync message will be displayed on the Main Filter.

• Clicking Resync to Filter s will reset both products and locations to those matching the purchase order selections.

Saving Your Filters

The entire set of filter selections can be stored for later retrieval. This is useful for creating watch lists of top items, bottom items, new locations, new product introductions, and so on.

To save a filter, enter a name into the Saved Filters field, then click the Save button.

Previously saved filter sets can be selected from the drop-down list. This will reload those saved filter selections in the Main Filter.

Use the Delete button to permanently remove the filter set that is currently selected in the drop-down list.

Figure 1-11 Saved Filters

Time Frame

The time frame of the Plan View can be set using the From and To date fields at the top.

Pivot Table

The pivot table displays the time-phased plan for the item/locations that fall within the selected filters. You view those filtered results aggregated to a chosen level of product and location hierarchies.

You also set and modify how the product, location, time, and values are pivoted in your view so you can analyze your inventory multiple ways.

Pivoting:

1. Click the pivot configuration toggle above the table to open the configuration drawer.

Page

Based on the identified need and the volatility in future demand, this is the additional units that are needed to meet the desired service level and prevent stock-outs

It has no aggregation method on non-Calendar hierarchy.

The aggregation method is Max from day to week.

• Safety Stock % of Demand??

  • This metric is calculated as the % of Safety Stock units in terms of Total Demand over review time.

It has no aggregation method on non-Calendar hierarchy.

The aggregation method is Max from day to week.

• Promotions

  • The Promotions metric shows the number of promotions for that time period. The Promotions are interfaced from RAP.

The aggregation method is Count.

• Presentation Stock

  • Presentation Stock is the minimum amount of stock required to fill a facing in the store. This can be an input from business strategies or interfaced from MFCS.

It has no aggregation method on non-Calendar hierarchy.

The aggregation method is Max from day to week.

• Demo Stock

Demo Stock is an extra quantity of an item desired at a location. It is considered sellable.

Commonly, this is used for a particular product that is made available to customers to demo in the store, but it could be used for other purposes as well. This can be an input from business strategies or interfaced from MFCS.

It has no aggregation method on non-Calendar hierarchy.

The aggregation method is Max from day to week.

Demand Metrics

The demand metrics help you understand what inventory quantity is expected to be consumed from a location—either from forecasted sale, back orders, or planned transfers to other locations.

• Demand

This is the forecasted demand calculated from IPO Demand Forecasting or aggregated from destination’s unconstrained order forecast.

For Stores, this is the approved IPO demand forecast.

For Warehouses, it is the aggregated order forecast from destinations.

The aggregation method is Sum.

• Total Demand

Total of all unconstrained demand used to calculate safetystock and forecast based replenishment boundaries.

For Stores, this is the forecast demand (the same as in Demand )

For warehouses, this is the aggregation of unconstrainted order forecasts from all destinations plus Additional Demand where flagged for inclusion.

The aggregation method is Sum.

• Constrained Demand

Constrained demand represents the demand that is met by the projected inventory. It is used to calculate inventory positions (and order boundaries within lead time).

For Stores this is the forecast demand met by the projected inventory.

For warehouses, this is the destination demand met from the warehouse. It is also the total order qty to destinations in the displayed time period.

The aggregation method is Sum.

• Backorder

This is the inventory that was already sold but could not be fulfilled due to there being no physical inventory at the time of sale. This is interfaced from MFCS.

This is expected to be a point-in-time value, so it is not repeated every day. It can be placed on the first day of the time period displayed.

It has no aggregation method.

• Pack Unconstrained Demand

This is the demand on the warehouse rounded to ideal packs. This is used to calculate replenishment pack need at the warehouse.

• Pack Constrained Demand

This is the actual units given from the warehouse according to the available packs. The difference between Pack Constrained Demand and Constrained demand tells you the Eaches fulfillment from the warehouse.

Inventory Metrics

Inventory metrics show what inventory is expected to arrive from open orders; what IPO recommends receiving; finally, based on forecasts, what the overall inventory levels are projected to be when following the order recommendations.

• Receipts Expected

On-Order Inventory at the destination location shown on the day that it should arrive.

The aggregation method is Sum.

• Receipt Forecast

Inventory receipt that is that is recommended by IPO and constrained by source availability. The visibility of this inventory movement is limited to IPO until the order day is reached and the order is approved. The Receipt Forecast shows inventory on the day that it should arrive at the destination location.

The aggregation method is Sum.

• Order Forecast

Shows the constrained order recommendation on the day the order should be approved within IPO and sent to MFCS for execution. It is identical to Receipt Forecast but shown on the order date rather than receipt date.

Location order forecasts are also aggregated to Supplier so you can review total recommended supplier purchase order quantities by order date.

• Inventory (BOP)

Beginning of day inventory at a location. It is Stock on Hand, or previous day’s Inventory (BOP) + all inbound inventory previous day – outbound inventory previous day.

Inbound inventory is Receipts Expected and Receipts Forecast.

Outbound inventory is Constrained Demand and open order quantity waiting for shipment (that is, allocations)

Inventory is never negative.

The aggregation method is Sum on non-Calendar hierarchy.

The aggregation method from Day to Week is First.

• Net Inventory

This is a calculation of the inventory projected to be available to meet the target stock level for a review period. This is an order-day value. It is the inventory compared to OP/OUTL during replenishment.

There is no aggregation method on non-Calendar hierarchy.

The aggregation method from day to week is Min.

• Pack Total Avail. Units

This provides the sum of units belonging to a pack. These units cannot be transferred as Eaches.

• Lost Sales

This is today’s Inventory (BOP) + inbound ( Receipt Forecast, Receipt Forecast ), minus outbound ( Total Demand ).

The aggregation method is Sum.

• Lost Sales % of Demand

This is calculated as % of: Lost Sales divided by Total Demand .

The aggregation method is Recalc.

• Pack Unconstrained Demand

For a warehouse, this represents the ideal demand from its destination stores, calculated and rounded to the optimal pack quantities, without considering warehouse inventory availability. This is used to calculate the replenishment need for packs at the warehouse.

• Pack Constrained Demand

For a warehouse, this is the actual quantity that can be fulfilled from the warehouse based on the packs that are currently available. The difference between this and the Constrained Demand for the components indicates how many units are being fulfilled as individual eaches.

• Pack Total Avail. Units

For a pack SKU at a warehouse, this is the total number of component units available within that pack. These units are only available for transfer as part of the pack.

• Total Order from Packs

For a component SKU at a destination, this shows the total units of that component being received from all assigned packs. This value is included as part of the future inbound inventory.

• Holdback Stock

This metric displays the Holdback Stock quantity for a specific SKU, location, and calendar period. It represents the inventory that has been logically ring-fenced and is not available for general planning.

Data for the selected metrics will be shown in the table for the selected timeframe. In the Inventory Plan, you will review the warehouse inventory comparing forecast demand and store need to the warehouse’s availability. Alerts and replenishment needs are also calculated on the sellable SKU.

When a single SKU is displayed, click the Advanced Option icon next to the Page cycle controls to open the Advanced Options Panel.

6. The Edit Area is displayed.

7. Update the Min and Max fields as desired, along with the start and end dates for the time period for the changes.

8. Click Apply to redisplay the policy tables with the updates.

9. A new row appears in a unpublished table for the optimization settings.

10. Click the Publish button at the bottom of the AO Panel (see AO Panel Main Buttons).

11. When the processing is completed, a Publish Completed notification is created (see Notifications).

12. To see a plan that contains the updates, view a plan that contains the same product, locations, and time frame (see View a Plan).

Override Service Levels

To override Service Levels, complete the following steps:

1. View a plan that contains the desired product, locations, and time frame (see View a Plan).

2. Click the Aggregate table in the Stores Tab column header for a week that overlaps the time that you want to alter.

3. The AO Panel is displayed. The level and location do not require updating (see Advanced Options Panel).

4. Scroll down until the Service Level policy type is visible.

5. Click the Override button to the right of the Service Level table (see Policy Tables).

6. The Edit Area is displayed.

7. Update the Service Level field as desired, along with the start and end dates for the time period for which you want to make the changes.

8. Click Apply to redisplay the Policy tables with the updates.

9. A new row is displayed in an unpublished table for the Service Level.

10. Click the Publish button at the bottom of the AO Panel (see AO Panel Main Buttons).

11. When the processing is completed, a Publish Completed notification is created (see Notifications).

12. To see a plan that contains the updates, view a plan that contains the same product, locations, and time frame (see View a Plan).

Override Inventory Selling Days

To override Inventory Selling Days, complete the following steps:

1. View a plan that contains the desired product, locations, and time frame (see View a Plan).

2. Click the Aggregate table, in the Stores Tab column header for a week that overlaps the time that you want to update.

3. The AO Panel is displayed. The level and location do not require updating (see Advanced Options Panel).

4. Scroll down until the Optimization Settings policy type is visible.

5. Click the Override button to the right of the Optimization Settings table (see Policy Tables).

6. The Edit Area is displayed.

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Header Section

At the top of the report, there is a header featuring three key performance tiles that summarize critical metrics related to out-of-stock situations:

• Total Lost Sales (R): Displays the total value of lost sales in retail currency (R) due to stockouts across all relevant products and locations within the selected time frame.

• Average Items Out of Stock: Shows the average number of distinct items that were out of stock, providing an indicator of the overall breadth of stockout issues.

• Out of Stock Days (Max): Indicates the maximum number of consecutive days an item was out of stock, helping to identify severe or prolonged stock availability issues.

These tiles serve as quick, high-level indicators of the overall impact of stockouts on business performance based on the filter selected by the user.

Canvas 1 – Lost Sales (Retail) by Class and Location

The first canvas provides a visual representation of lost sales in retail value segmented by product class and location. A radial chart (or circular chart) is displayed on the top-left side of the canvas.

The chart illustrates Lost Sales (R), broken down by Class and Location (store or distribution center), allowing users to quickly identify where the highest financial losses due to out-of-stock situations are occurring.

Canvas 2 – Lost Sales (Unit and Retail) by Subclass and District

The second canvas offers a comparative analysis of lost sales, focusing on both unit and retail value metrics:

A bar chart is used to display:

• Lost Sales (U): Total quantity of units lost due to stockouts.

• Lost Sales (R): Total retail value of the lost sales.

These metrics are segmented by District and Subclass.

District represents the higher-level grouping above the Location level at location hierarchy, and Subclass the higher-level grouping above Style level.

This view helps users analyze how different geographical areas and product segments contribute to lost sales, supporting more targeted inventory management strategies.

Canvas 3 – Detailed Data Table

The third canvas contains a comprehensive data table that provides detailed, time-phased metrics for in-depth analysis:

The table includes the following key metrics:

• LOST_SALES_RETAIL: The monetary value of sales lost due to out-of-stock situations.

• LOST_SALES_UNITS: The number of units that could not be sold because of stockouts.

• ALERT_DAYS_COUNT: The number of days during which the stockout condition was active.

Data is presented in a time-phased format, showing the specific days when lost sales occurred, allowing users to identify patterns, trends, or recurring issues over time.

• Avg Items Low Stock: Displays the average number of distinct items categorized as Low Stock, providing insight into the overall breadth of stock issues.

• Low Stock Days (Max): Indicates the maximum number of consecutive days an item was out of stock, helping to identify severe or prolonged stock level problems.

These tiles offer quick, high-level indicators of the overall impact of Low Stock on business performance, tailored to the filters selected by the user.

Canvas 1 – Low Stock (Unit) by Class and District

The first canvas offers a visual representation of Low Stock quantities, segmented by product class and district. A bar chart, positioned in the top-left corner of the canvas, illustrates Low Stock (U) broken down by both class and district, enabling users to quickly identify where the highest concentrations of Low Stock are occurring.

Canvas 2 – Low Stock (Unit and Retail) by Subclass and District

The second canvas offers a comparative analysis of Low Stock, focusing on both unit and retail value metrics.

A bar chart is used to display:

• Low Stock Units (U): Represents the total number of product units flagged as Low Stock within the reporting period, based on the predefined thresholds set in the Alert Management tool. This metric helps quantify the physical extent of inventory shortages across subclasses and districts.

• Low Stock Retail (R): Represents the total retail value of all products classified as Low Stock, calculated by multiplying the unit retail price by the corresponding quantity of Low Stock units. This metric provides a financial perspective on the potential revenue at risk due to Low Stock conditions.

These metrics are segmented by District and Subclass.

This view helps users analyze how different geographical areas and product segments contribute to the low level of stock, supporting more targeted inventory management strategies.

Canvas 3 – Detailed Data Table

The third canvas contains a comprehensive data table that provides detailed, time-phased metrics for in-depth analysis:

The table includes the following key metrics:

• LOW_STOCK_RETAIL: The monetary value of low level of invetory.

• LOW_STOCK_UNITS: The total number of product units classified as Low Stock based on their relationship to the demand forecast. This metric indicates instances where available inventory falls below the expected demand threshold, signaling potential risks of stockouts and unmet customer deman.

• ALERT_DAYS_COUNT: The number of days during which the Low Stock condition was active.

Data is presented in a time-phased format, showing the specific days when low stock occurred, allowing users to identify patterns, trends, or recurring issues over time.

Additionally, the table displays the hierarchical structure above the Subclass level (Class and Department)

Canvas 1 – Overstock (Retail) by Class and Location

The first canvas provides a visual representation of overstock in retail value segmented by product class and location. Positioned in the top-left corner of the screen, a radial (circular) chart illustrates Overstock (R) , broken down by both class and location (store or distribution center) enabling users to quickly identify areas with the greatest financial impact resulting from overstock situations.

Canvas 2 – Overstock (Unit and Retail) by Subclass and District

The second canvas offers a comparative analysis of overstock, focusing on both unit and retail value metrics:

A bar chart is used to display:

• Overstock (U): Total quantity of units classified as overstock.

• Overstock (R): The total retail value of units classified as overstock, calculated by multiplying the quantity of overstocked items by their respective retail prices.

These metrics are segmented by District and Subclass.

Canvas 3 – Detailed Data Table

The third canvas contains a comprehensive data table that provides detailed, time-phased metrics for in-depth analysis.

The table includes the following key metrics:

• OVER_STOCK_RETAIL: The monetary value of inventory classified as overstock, representing the total retail worth of surplus stock exceeding the defined thresholds.

• OVER_STOCK _UNITS: The total number of units exceeding the predetermined stock level, indicating the quantity of inventory above the acceptable stock limits.

• ALERT_DAYS_COUNT: The number of days during which the overstock condition persisted.

Data is presented in a time-phased format, showing the specific days when overstock occurred, allowing users to identify patterns, trends, or recurring issues over time.

Additionally, the table displays the hierarchical structure above the Subclass level (Class and Department).

This detailed breakdown enables users to perform granular analyses of alert events, understanding not only when they occurred but also how they relate to the broader organizational structure of products and locations.

Capacity Constraint

The Capacity Constraint alert is triggered when a proposed allocation, transfer, or replenishment order would send a quantity of items to a location that exceeds its defined storage capacity. This feature helps prevent sending too much product to a store or warehouse, ensuring merchandise can be processed efficiently and placed on the sales floor rather than getting stuck in a backroom.

When a capacity constraint is violated:

• An alert is triggered to notify the user.

• The system will not auto-approve any replenishment or allocation that triggered the alert.

• Users must manually review the flagged order, where they can choose to modify the quantity or override the constraint to approve the stock movement.

Resolve Alerts

Many inventory issues result from a difference in sales compared to the forecast. These issues tend to be resolved reactively with substitutions, manual inventory transfers, or markdowns. Other issues arise from unplannable factors outside of your control such as product recalls, manufacturing issues, or shipping delays or errors. Similar manual resolutions ensue.

However, several potential issues can be seen when inventory projections are made into the future. These potential issues can be resolved proactively before they manifest into an actual shortage or overstock.

• Effectively defined alert thresholds ensure that these issues will be highlighted for you.

• Other key items, hot buys, or new product introductions that are on your daily or weekly towatch list may not break the alert thresholds but can follow the same analysis to adjust inventory to a more suitable level.

While each situation requires expert knowledge of the business goal, use the following recommendations to get started considering the best resolution to projected inventory issues. These recommendations may resolve a particular problem; however, the same frequently occurring inventory issue will require additional analysis of the supply chain inventory levels, supplier compliance, and potential shipping issues before resorting to a long term policy override.

• Use the Plan View to review the forecasted sales and inventory.

• Where an inventory issue is identified, use the Order Point (OP) and Order Up to Level (OUTL), on or just before the problem occurs, to identify when the minimum (OP) and target inventory levels (OUTL) do not align with forecast sales and desired service level.

• When OP or OUTL requires adjusting, open the AO Panel by clicking in the cell of the review day/item/location(s) that require adjustment.

• When an issue occurs in a large concentration of stores, the warehouse stock levels could be an issue. Consider reviewing the warehouse replenishment policies and follow the same recommendations.

Issue	Replenishment Method	Policy Adjustment
Out of Stock	Min/Max	• Create a Stock Level Override, set Increment % to a value greater than 1 to increase OP / OUTL to align with increased forecasted sales.
	Time Supply	• Ensure that Max Safety Stock Units/Days is not constraining OP, allowing inventory to get too low before re-ordering. • Create an Optimization Setting override, increase Inventory Selling Days (relative to Max Time Supply) to increase OUTL.
	Dynamic	• Ensure that Max Safety Stock Units/Days is not constraining OP, allowing inventory to get too low before re-ordering. • Create an Optimization Setting override, increase Inventory Selling Days to increase OUTL
Low Stock	Min/Max	• Create a Stock Level Override, set Increment % to a value greater than 1 to increase OP / OUTL to align with increased forecasted sales.

Issue	Replenishment Method	Policy Adjustment
	Time Supply	Ensure that Max SafetyStockUnits/Days is not constraining OP, allowing inventory to get too low before re-ordering. • Optionally: – Create an Optimization Setting override, increase Inventory Selling Days (relative to Max Time Supply) to increase OUTL. This creates a fixed target for inventory days of supply. – Or create an Optimization Setting override, set Min Safety Stock Units or Days to a value that helps to carry extra stock for volatile sales or shipping delays. This allows inventory days of supply to more flexibly adjust while ensuring a minimum level of safety stock is achieved to reduce the chance of an out of stock.
	Dynamic	Ensure that Max SafetyStockUnits/Days is not constraining IP, allowing inventory to get too low before re-ordering. • Optionally: – Create an Optimization Setting override, increase Inventory Selling Days to increase OUTL. This creates a fixed target for inventory days of supply. – Or create an Optimization Setting override, set Min Safety Stock Units or Days to a value that helps to carry extra stock for volatile sales or shipping delays. This allows inventory days of supply to more flexibly adjust while ensuring a minimum level of safety stock is achieved to reduce the chance of an out of stock. • In rare cases, create a Service Level override to increase Service Level for key in-stock periods (for example, holiday)
Overstock	Min/Max	• Create a StockLevelOverride, set Increment % to a value less than 1 to decrease OP / OUTL to align with a slow period of forecastedsales.
	Time Supply	• Ensure that Min SafetyStockUnits/Days is not increasing OP undesirably. • Optionally: – Create an Optimization Setting override, decrease Inventory Selling Days (relative to Min Time Supply) to decrease OUTL. This creates a fixed target for inventory days of supply. – Or create an Optimization Setting override, set Max Safety Stock Units or Days to a value that helps limit the ordering frequency by helping to limit OP.

Issue	Replenishment Method	Policy Adjustment
	Dynamic	• Ensure that Min SafetyStockUnits/Days is not increasing OP undesirably. • Optionally: – Create an Optimization Setting override, decrease Inventory Selling Days to decrease OUTL. This creates a fixed target for inventory days of supply. – Or create an Optimization Setting override, set Max Safety Stock Units or Days to a value that helps limit the ordering frequency by helping to limit OP. • In rare cases, or nearing end of life, create a Service Level override to decrease Service Level. This will reduce inventory but could increase risk of out of stocks if actual sales increase.
Capacity Constraint	Min/Max	• Create a Stock Level Override, set Increment % to a value less than 1 to decrease OP / OUTL to align with the location’s physical storage capacity.
	Time Supply	• Ensure that Min Safety Stock Units/Days is not increasing the OP to a level that contributes to exceeding the location’s physical capacity. • Optionally: – Create an Optimization Setting override to decrease Inventory Selling Days (relative to Min Time Supply) to lower the OUTL. This aligns the target inventory level with the physical storage limits of the location. – Or create an Optimization Setting override to set a Max Safety Stock Units or Days. This acts as a hard ceiling on buffer inventory to prevent the total stock from breaching the location’s physical capacity.
	Dynamic	• Ensure that Min Safety Stock Units/Days is not increasing the OP to a level that contributes to exceeding the location’s physical capacity. • Optionally: – Create an Optimization Setting override to decrease Inventory Selling Days to lower the OUTL. This aligns the target inventory level with the physical storage limits of the location. – Or create an Optimization Setting override to set a Max Safety Stock Units or Days. This is the most effective tool, as it places a hard ceiling on dynamically calculated buffer stock to ensure the total inventory remains within physical constraints. • In cases where a location’s physical capacity makes the current service level unattainable, create a Service Level override to decrease the Service Level. This strategically reduces the inventory target to a level that can be physically stored, acknowledging a higher risk of stock-outs.

Glossary of IPO Terms

This table provides definitions for many of the terms that are used in the IPO tables in the UI.

Table 1-2 IPO Term Definitions

Term	Definition
Activate Date	For seasonal items and items with a short life cycle, the activate date indicates the date that a given item/location should be considered for replenishment.
Actual Sales Units W-1	Actual sales unit for a given item/location during the last calendar week prior to the date that the inventory optimization ran.
Actual Sales Units W-2	Actual sales unit for a given item/location during the last two calendar weeks prior to the date that the inventory optimization ran.
Actual Sales Units W-3	Actual sales unit for a given item/location during the last three calendar weeks prior to the date that the inventory optimization ran.
Actual Sales Units W-4	Actual sales unit for a given item/location during the last four calendar week prior to the date that the inventory optimization ran.
Allocation Name	A user-defined name given to a manual allocation for easy identification and tracking within the system.
Back-order Qty at Dest.	The back-order quantity at the destination location for a given item.
Back-order Qty at Origin	The back-order quantity at the origin location for a given item. This field will be blank for a PO because the origin is a supplier.
Calculation Status	A system-generated status that indicates the current state of the allocation’s quantity calculation. Common statuses include ‘Not Calculated’, ‘Calculating’, ‘Calculated’, and ‘Calculation Error’.
Coverage Per. End DT	Coverage period end date, that is lead time plus review time after recommendation date.
Coverage Per. Start DT	Coverage period start date, that is lead time after recommendation date.
Deactivate Date	For seasonal items and items with a short life cycle, the deactivate date indicates the date that a given item/location should be stopped being considered for replenishment.
Demand Source	A policy setting within a manual allocation that defines the source of demand data (for example, Forecast, Sales Plan) used to calculate the gross need at each destination.
Fcst. Coverage Per.	Forecast sales units for the coverage period.
Fcst. LT	Forecast sales unit for the period of lead time.
Fcst. Sales Units W+0	Forecast sales unit for the current week.
Fcst. Sales Units W+1	Forecast sales unit for the next week.
Gross Need	The total, unconstrained demand for an item at a destination location, calculated based on the selected Demand Source and date range, before considering any existing inventory.
Holdback Stock	A specific quantity of inventory at a warehouse that is intentionally withheld from the general inventory pool to support strategic initiatives.
Holdback Stock at Dest	The Holdback Stock quantity of the destination for a specific SKU.
Holdback Stock at Origin	The Holdback Stock quantity of the source warehouse for a specific destination and SKU.
In-transit Qty at Dest.	The in-transit inventory for the destination location for a given item.

Table 1-2 (Cont.) IPO Term Definitions

Term	Definition
In-transit Qty at Origin	The in-transit inventory for the origin location for a given item. This field will be blank for a PO because the origin is a supplier.
Lead Time	The expected number of days required to move the item from the primary source to the destination location.
Loc. Type	Location type can be Store or Warehouse.
Manual Qty	A user-entered value in the allocation results screen that overrides the system-calculated quantity. This allows the allocator to make final adjustments to the distribution plan based on business knowledge.
Net Need	The calculated inventory required at a destination after accounting for existing and in-transit stock. It is typically calculated as ‘Gross Need’ minus the relevant on-hand inventory positions.
Next Review Date	For a given item/location, the next review date is the date that the item is going to be reviewed for replenishment based on its review schedule.
OH Qty at Dest.	The on-hand inventory at the destination location for a given item.
OH Qty at Origin	The on-hand inventory at the origin location for a given item. This field will be blank for a PO because the origin is a supplier.
On-order Qty at Dest.	The on-order inventory at the destination location for a given item.
On-order Qty at Origin	The on-order inventory at the origin location for a given item. This field will be blank for a PO because the origin is a supplier.
Pack Allocation Tuning Objective	A setting for Multi-Size Packs that guides how the system assigns packs when a perfect match to the size-level need is not possible. Options include: ‘Balance over and under-allocation’, ‘Over-allocate is preferred’, and ‘Under-allocate is preferred’.
Pack Fill Rate %	The percentage of a pack’s total component quantity that is consumed by a store’s unfulfilled need. This metric is used to find the best-fitting packs, with 100% being an ideal match.
Preferred Pack	An optional parameter that instructs the system to prioritize a specific Pack ID when calculating the unconstrained need for a Multi-Size Pack.
Primary Source	The primary source of replenishment for a given item/location. If the item/location has a stock category of warehouse-stocked (or cross- docked), the primary source will indicate the warehouse from which (through which) the item will be sourced. If the item has a stock category of direct to store/warehouse, the primary source will indicate the supplier.
Replenishment Method	The method that was used in calculating the RP/RUTL.
Worksheet	The status of a manual allocation that has been saved as a draft but not yet approved. Allocations in a ‘Worksheet’ status can be opened and edited at any time.
Review Time	Number of days between the day that the inventory optimization runs and the next time that a given item/location is scheduled for review. For example, if an item/location has a daily review schedule, the review time will be one day.
RP (Re-order Point)	Level of inventory that is used as a threshold to recommend an order.
RUTL (Receive Up-to Level)	When the inventory level falls below the re-order point, an order is recommended to raise inventory to this level.

Table 1-2 (Cont.) IPO Term Definitions

Term	Definition
System Order Qty	The recommended order quantity for a PO/Transfer. The system order quantity is calculated by the optimization algorithm and is the quantity that takes into account all constraints such as RP, RUTL, space capacity, and available supply at the origin.
Target Service Level	This is the value of the service level that is used in the safety stock calculation. Service Level is defined as the percentage of unit demand that should be met by inventory. Valid values are between 0 and 1. For example, a service level of 0.9 indicates that RP and RUTL should be optimized such that on average 90% of the demand is met.
Unconst. Need	The unconstrained need is the quantity that is required to bring the inventory up to the RUTL. It does not take into account the shelf space capacity and the potential shortage of supply at the origin.
User Order Qty	This is the user override for the quantity of the recommended PO or transfer order.

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In this guide

• Guide: Inventory Planning Optimization User Guide
• Next: A Appendix: Oracle Retail AI Foundation Cloud Services Overview

Related chapters

• 15 Inventory Planning Optimization — AI Foundation Implementation Guide · shares ADMINISTRATOR_JOB, INVENTORY_ANALYST_JOB