AIML Predictive Fulfillment Dashboard
The AI/ML Predictive Dashboard allows you to make well informed decisions based on past order data to view future Order Cycle/Processing/Waiting Time predictions. This information comes from the most recent prediction from your model in AIML Prediction Run.
The predictive dashboard displays the average order cycle time in hours. We have included various KPIs that gives insight on how your orders have and will move out of the warehouse. By clicking on the order status, it will further filter and display the order type by status.
Your orders above and below your set target threshold will be shown in a datagrid that displays the following information:
- Order Number
- Status, Order Type
- Predicted Time
- Expected Shipping Date
- Required Shipping Date
The ratio of orders above and below your target threshold will also be shown in a pie chart above the data grid.
Steps to enable:
- Navigate to the UI and click on the username dropdown.
- Select Redwood UI Prototype.
- All the data should be there.
Please note that only the Default Template will display data in the Predictive Dashboard. If no default template is configured, the predictive dashboard will not return any results.
You can access the AI/ML Predictive Fulfillment Dashboard by clicking the on the username.
Oracle Market Basket Analysis
We’ve introduced Market Basket Analysis (MBA) in Oracle Warehouse Management to help you gain deeper insights into your inventory and customer buying patterns. This AI-powered feature identifies items frequently ordered together, enabling smarter decisions when it comes to inventory placement and putaway strategies.
By understanding these patterns, you can optimize slotting, reduce picker travel time, and increase overall warehouse efficiency.
What It Does
Market Basket Analysis leverages Oracle’s advanced AI/ML algorithms to analyze historical order data. It detects relationships between products and groups them into “frequent item sets” based on how often they are ordered together and the likelihood that this trend will continue.
The result is a clear, actionable report that helps you:
- Identify product pairings or groupings.
- Improve inventory layout by slotting complementary items together.
- Streamline picking operations based on actual order behavior.
There are two key metrics when running a Market Basket Analysis, support and confidence. The system ranks items based on support and then confidence, this means that if supports are equal between items, then they will be based on confidence after.
Support measures how frequently an itemset appears in the dataset. It’s calculated as the proportion of transactions that contain a specific item or combination of items out of all transactions.
Confidence indicates the likelihood that item B is purchased when item A is purchased. It’s calculated as the ratio of transactions containing both A and B to the number of transactions containing just A.
- Create a scheduled job to generate the training data for Market Basket Analysis in the Scheduled Jobs UI.
- Navigate to the AI/ML Training Data to verify that the training data contains the correct data.
- Create a Market Basket Analysis Training Template in the AI/ML Training Template UI
- After creating your Market Basket template with all of the mandatory fields completed, click Train Template.
- Navigate to the AI/ML Model UI and select the most recent Market Basket Analysis entry. In the “Model Reference” field, click on the hyperlink.
- You can now view the results of your successful Market Basket Analysis.