Embedded Machine Learning Overview

Predicting shipment transit times and order routes can be a challenge in today's ever changing environment. Logistics Machine Learning (LML) allows you to use historical data to predict end-to-end transit times for direct shipments and to predict order routes. LML leverages OTM historical shipment and order data with optimized algorithms and machine learning techniques to deliver highly accurate predictions.

Two platforms can be used to predict transit times and order routes depending on how OTM is configured:

• Embedded Learning
• External Service (for use only with IoT IA services and can be used by migrated instances only. This is being deprecated.)

Note: Use of an external service to connect to IoT IA services has been deprecated and will be removed in a future release. IoT IA is being replaced by Embedded Learning.

The Logistics Machine Learning transit time estimation and order route prediction processes are continuous processes in which the information gained from the results of machine learning training is used to both make changes to current process and to update setup data that can then be used to enhance machine learning model training.

Let's review the high-level flow:

1. Data Setup: Machine learning needs data as input. LML provides historical OTM data via machine learning projects and machine learning scenarios. This data can include shipment and tracking event data and orders and related data.
2. Data Export and Pre-Training Analytics: Export this historical data to Oracle Transportation Intelligence (OTI) and review it to see if you want to change the data setup.
3. Training and Post-Training Analytics: Send machine learning scenario data to the embedded Oracle Machine Learning database for training. Then, review the learning results.
4. Prediction:
   • Select groups of OTM shipments and obtain a predicted transit time for each shipment.
   • Select OTM orders and obtain a predicted order route.
5. Evaluation: Finally, you evaluate all of this data. Set up OTM process management to automate the above steps.

Embedded Machine Learning (EML) Process Flows

Let's take a deeper look at the process and see how the order or truckload (TL) shipment related data moves between OTM, OTI, and EML.

1. Set up, Data Extraction (Export), and Loading
   
   This flow involves:
   1. In Logistics Machine Learning, you set up your machine learning project. The project collects historical data via TL shipment or order saved queries.
   2. Still in LML, you export the data from OTM and send to Logistics Machine Learning Intelligence. In this step, LML extracts the data that the project collects from OTM and loads it into Logistics Machine Learning Intelligence.
   3. Optionally, you can view the data in OTI using Logistics Machine Learning Intelligence or the dashboard, correct the data as necessary in OTM, and repeat step b to export the updated data to Logistics Machine Learning Intelligence.
2. Train a Scenario
   
   Now that your data is uploaded to the HDOWNER database, you are ready to tell EML to train the machine learning model using this data.
   1. EML: Use a machine learning scenario.
   2. LML to EML: Perform training on these sub-sets of data via the LML action Perform Training. Training is performed on the EML server. Once the model is trained, you can review the results of the training.
3. Prediction

And finally, you are ready to use the trained model to predict TL shipment transit times or to predict order routes using the Perform Prediction action.

4. System Log IDs are available to help troubleshoot EML issues.

Enabling Embedded Machine Learning

Oracle Transportation Intelligence (OTI)

1. Ensure that OTI is installed and the Run ETL action has been performed.
2. Provide access to Logistics Machine Learning Intelligence subject area and dashboard.