Performing Slotting

Objective

After completing this lesson, you will be able to perform the slotting process.

Slotting

The goal of slotting is to help in the determination of the best storage bin for a material during putaway. The best bin for a material depends on factors such as whether that material is fast-moving, slow-moving, and so on. A well laid-out warehouse makes the picking process easier.

Slotting determines the ideal putaway parameters such as storage types, storage sections, and bin type for a product in a particular warehouse. This information is stored in the warehouse product.

Examples of data that the slotting process uses to determine putaway, includes:

  • Product data

  • Storage requirement data

  • Packaging data

  • Demand forecasts or historical data from SAP Advanced Planning and Optimization (SAP APO)

During slotting, the system determines the following storage parameters:

  1. Putaway control indicator (and, optionally, stock removal control indicator)

  2. Maximum quantity in storage type

  3. Storage section indicator

  4. Storage bin types

The system always performs step 1. Steps 2 to 4 are optional. Steps 1, 3, and 4 are performed using the condition technique. Step 4 can also be performed using storage bin type determination rules..

During slotting, the system determines the storage parameters and stores them in the warehouse product. They are stored either as planned values (for the future) or you can update the warehouse product immediately.

Product Related Data

The following is an example of product-related data that can be used as input for slotting.

Product Related Data Used for Slotting

FieldValue
Storage condition3 (not outside)
Theft-proneN
Handling code200 (metal)
Demand quantity700 (per month)
Number of order lines50
Recommended storage quantity2100
Storage class13 (Non-inflammable solids)
Water pollution classification1 (Minimal water pollution danger)
Nesting factors0.5
Package typeDefault packaging material (Wooden pallets)
Material length0.05m
Material width0.01m
Material height0.01m
Material weight10g
Packaging length0.80m
Packaging width1.0m
Packaging height1.0m
Packaging weight75kg

Calculate Parameters

To calculate parameters, such as the maximum quantity in the storage type, slotting needs product-requirement data or the forecasted demand for the relevant product.

Demand data is useful when slotting is used to relocate products based on whether they are fast-moving or slow-moving.

This requirement or demand data can come from SAP Advanced Planning and Optimization (APO), you can enter the data manually, or you can enter the data by mass maintenance. SAP Extended Warehouse Management (EWM) stores this information locally in the warehouse product (on the Slotting tab in the Requirement/Demand Data area).

When using SAP APO planning data, it is important to realize that SAP APO and SAP EWM manage requirements data at different organizational levels. SAP APO performs planning processes for locations of the type Plant. SAP EWM stores requirement data at the warehouse number level. Slotting assumes that a one-to-one relationship exists between the SAP APO location and the SAP EWM warehouse number. If the relationship is not one-to-one, you can use Business Add-Ins (BAdIs) for the conversion.

Slotting Process

The overall slotting process is shown in the following figure.

Slotting Process

Updating Product Master Data for Slotting

Whether the product master data is updated or not, and if the values are adjusted as active values or planned values, depends on the save mode that you use when slotting. Three options are offered as follows:

  • Do not save results

  • Save results

  • Save and activate results

Do not save results: You should use this option if you have performed slotting interactively and you want to analyze the results. (If you have performed slotting in the background, you can analyze the log, but you cannot see the results directly.)

Save results: Depending on the storage bin and stock situation, it can be useful to save the results of slotting as planned values only. The system only keeps the planned values for possible activation, and does not use them in storage processes. (The system only updates the planned values in the warehouse product.)

Save and activate results: The system updates the results of slotting in the fields for the planned values and the active values in the warehouse product. These new values are then available immediately for all storage processes. (The system updates both the planned values and the active values in the warehouse product).

Slotting by Machine Learning

Slotting by machine learning allows you to analyze the warehouse product master settings of the existing products and propose the storage concept for new or changed products. This can reduce the effort of initial setup, with lower implementation effort to derive slotting rules automatically from the warehouse setup and product master. You don't need to adjust your slotting rules because of changes in warehouse processes and setup.

A machine learning workflow for product slotting uses historical data to train and retrain models, which infer master data for optimized slotting and demand planning.

The machine learning algorithm generates a statistics-based model from representative input data. The generation of the model is called training. The system iteratively improves the model by retraining it after any changes to specific input values.

During slotting by machine learning, the system determines the following storage parameters:

  • Putaway control indicator
  • Stock removal control indicator
  • Storage section indicator

ABC Analysis

ABC analysis in SAP Extended Warehouse Management (EWM) allows you to categorize the importance of products based on confirmed (product) warehouse tasks.

ABC analysis table categorizes products based on their frequency, highlighting how a few items account for most activity; visual suggests data-driven insights.

Note

ABC analysis is meant for processes and warehouse operations that you can still oversee without using sophisticated BI techniques. It can be used in combination with slotting. In SAP EWM, the main functions for classification are available in slotting.

The advantage of the ABC analysis is that it does not required condition records and that it is based on historic data. Slotting reads demand data from APO Demand Planning, but therefore slotting can also use other information and update more indicators and parameters.