Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx
Technology Category
- Analytics & Modeling - Machine Learning
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Cement
- Transportation
Applicable Functions
- Logistics & Transportation
Use Cases
- Intelligent Packaging
- Vehicle-to-Infrastructure
Services
- Data Science Services
About The Customer
GHD is a global consulting company that was contracted by the state government in Melbourne, Australia. The company was tasked with improving the container supply chain processes by collecting and understanding large datasets from industry, government, and transport software service providers. The machine learning team at GHD, led by Nikita Atkins, Data Science Global Leader, was responsible for this project. They used Alteryx to narrow down 100 million shipping container and commodity records to 1.9 million with a 99.9965% match accuracy rate. They also used the Intelligence Suite to forecast and build predictive models to better estimate various aspects of the container supply chain.
The Challenge
The Port of Melbourne (PoM) in Australia is mandated to track all shipping containers that enter and exit every five years. This data is crucial for ensuring the right infrastructure, industrial land, planning controls, and policy settings are in place to support efficient supply chains. However, the PoM was using over 57 independent groups to track the data in more than 60 different formats. This process was not only time-consuming, requiring hundreds of hours of manual work, but also inefficient, with a forecasting rate below 30%. Furthermore, they were unable to successfully perform a match analysis. The state government in Melbourne, Australia, therefore, contracted the machine learning (ML) team at GHD, a global consulting company, to improve these container supply chain processes.
The Solution
In 2019, the machine learning team at GHD, led by Nikita Atkins, developed a predictive modeling process using Alteryx. They gathered data from over 250,000 container trips from September to October 2019, standardized it, combined it, and de-duplicated 100 million records. They also included over 200 business rules before making the data consumable. The final data set of 1.9 million records was compared to the PoM data, and the data cleansing with Alteryx yielded a match of 99.9965%. Furthermore, they used Alteryx Intelligence Suite to build 10 predictive models. These models were used to estimate a container’s location, the commodities held, the capacity level of each container, and provide insight into a container’s return trip ending point and timeline. The results showed a 77% accuracy rate in tracking the trip cycle of commodities and shipping containers.
Operational Impact
Quantitative Benefit
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