Logistics Machine Learning Overview

Predicting shipment transit times 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. LML leverages OTM historical shipment data with optimized algorithms and machine learning techniques to deliver highly accurate predictions.

High-level Flow

The Logistics Machine Learning transit time estimation process is a continuous process 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 that is shown in the graphic below.

 

1. Data Setup: Machine learning needs data as input. LML provides historical OTM shipment and tracking event data via machine learning projects and machine learning scenarios.
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 Oracle's Internet of Things Intelligence Applications (IoT IA) machine learning service for training. Then, review the learning results.
4. Prediction: Select groups of OTM shipments and obtain a predicted transit time for each shipment from the IoT IA machine learning service.
5. Evaluation: Finally, you evaluate all of this data. Set up OTM process management to automate the above steps.

Process-level Flows

Let's take a deeper look at the process and see how the data moves between OTM, OTI, and IoT IA.

 

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 shipment and tracking event data via shipment saved queries.
   2. Still in LML, you export the historical shipment data to Logistics Machine Learning Intelligence in OTI. In this step, LML extracts the data that the project collects and loads it into OTI HDOWNER database.
   3. In OTI, you review the data in either the Logistics Machine Learning intelligence subject area or the dashboard, correct the data as necessary, and repeat step b to export the updated data to OTI.
   4. Back in LML, you send the data to IoT IA using the Export to Machine Learning Services action. This action saves the data sent to Analytics to a flat file, copies the flat file to the object store, and tells the IoT IA machine learning service to pick up the file. When IoT IA machine learning service picks up the file, it sends a response to LML.
2. Train a Scenario
   
   Now that your historical shipment and tracking event data is uploaded to the IoT IA server, you are ready to filter that data into smaller sub-sets, and tell IoT to train the machine learning model using these smaller sub-sets of the data.
   1. LML: Use a machine learning scenario to filter the data using shipment filters, exclude columns, and remove outliers.
   2. LML: Use the Load Shipment Data into Analytics action to send information to Analytics about the sub-set of shipments that will be used in training for a given scenario.
   3. LML to IoT IA: Perform training on these sub-sets of data via the LML action Perform Training. Training is performed on the IoT IA server. Once the model is trained, you can review the results of the training.
3. Predict Transit Times

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

4. System Log IDs are available to help troubleshoot LML issues.
5. This is a test

Enabling Logistics Machine Learning

Logistics Machine Learning involves several different Oracle systems which need to communicate with each other. OTM, OTI, OCI Object Store, and IoT IA are all included in the LML data flow. See the Setup Guide for Logistics Machine Learning for a detailed look at setting up Logistics 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.

IoT IA

1. Subscribe to IoT IA.
2. Complete the steps to integrate with the IoT Fleet Monitoring Cloud Service.
3. Set up access to the Oracle Storage Cloud Service.
4. Create an external system with the following:
   1. User Name and Password as provided by IoT IA
   2. Content Type: application/json
   3. Authentication Type: HTTP Authentication (Basic)
   4. URL as provided by IoT IA
5. Attach the external system to the Machine Learning Logic Configuration via the ML EXTERNAL SYSTEM ID parameter.

Oracle Storage Cloud Service

Along with your subscription to the Oracle Storage Cloud Service, you can set up a pre-authenticated request and get a pre-authenticated URL. Then, create an external system that uses this pre-authenticated URL.