Alteryx Case Studies Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx

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	Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx Alteryx

Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx

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
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. Read More
About 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. Read More
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. Read More Log in to view content
Contents

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

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.

About 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.

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.

Impact #1	The insights developed by GHD using Alteryx Intelligence Suite have provided the local and state government with a better understanding of where containers are going and where commodities are being purchased. This has significantly improved decision-making in transport infrastructure and network planning. As a result, the delivery of a faster, focused, and more productive supply chain has been enabled. The process has also significantly reduced the time and resources previously required for data tracking and analysis, making it a more efficient and effective system.

Impact #1

The insights developed by GHD using Alteryx Intelligence Suite have provided the local and state government with a better understanding of where containers are going and where commodities are being purchased. This has significantly improved decision-making in transport infrastructure and network planning. As a result, the delivery of a faster, focused, and more productive supply chain has been enabled. The process has also significantly reduced the time and resources previously required for data tracking and analysis, making it a more efficient and effective system.

Benefit #1	160 hours of data cleansing reduced to 30 seconds
Benefit #2	99.965% data accuracy match achieved
Benefit #3	77% increase in predictive capability

Benefit #1

160 hours of data cleansing reduced to 30 seconds

Benefit #2

99.965% data accuracy match achieved

Benefit #3

77% increase in predictive capability

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Overview

Revolutionizing Container Supply Chain Processes: A Case Study on GHD and Alteryx

Operational Impact

Quantitative Benefit