James Walter, Senior Data Scientist
Yohan Ko, Senior Data Engineer
Btou Zhang, Network Operations Lead
Duc Nguyen, Shift Production Manager
Normy Chamoun, Head of Processing NSW/ACT
Sheral Rifat, Data Science Manager
Phil Chan, Data Engineering Manager
David Barrett, Facility Manager
Boris Savkovic, Data Science Manager
Organization: Australia Post
Australia Post is a government business enterprise that provides postal services in Australia. We are also Australia’s leading logistics and integrated services business. Last year, we processed 2.6 billion items, delivered to 12.4 million delivery points across the nation, and continued to provide essential government and financial services via the country’s largest retail network.
We are Australia’s leading logistics and integrated services business. Last year, we processed 2.6 billion items, delivered to 12.4 million delivery points across the nation, and continued to provide essential government and financial services via the country’s largest retail network.
The global pandemic has accelerated e-commerce growth with more households shopping online than ever before. Whilst Australia Post has a long and proud history to lean on, we continue to face challenges from ever-increasing parcel volumes and a great digital disruption that is shaking up the wider logistics industry. This requires us to innovate and transform.
A key daily activity, at facilities within our logistics network, relates to shift production managers being tasked with making daily resource/staffing planning decisions, that seek to ensure that we process parcel demand in a timely manner, whilst controlling for cost. Currently, these decisions are being made based on limited, but best-available, information. Too few staffing hours can result in sub-optimal throughput and parcel delays, whilst too many staffing hours can unnecessarily increase labor spend.
To address this pain paint, our Data Science team developed a shift volume forecasting algorithm in Dataiku. The model provides facility operators with daily shift volume forecasts and translates this information into staffing requirements.
The algorithm was trialed in partnership with one of the biggest processing facilities in the Southern Hemisphere, Sydney Parcel Facility, and is now used to inform daily planning activities.
Feedback from shift production managers is that "based on the volume prediction, we were comfortable with not running overtime the following morning. This paid off". Thus the model is empowering managers to confidently make decisions regarding the need for overtime.
The approach is changing the way that facility operators make decisions, resulting in significant operational dollar value savings (~15 million Australian Dollars [AUD] p.a. once rolled out nationally).
We chose Dataiku as we were looking for an end-to-end data science platform that simplified and automated many aspects of the data science and data engineering workflow, allowing our team to deliver results faster and with fewer frustrations.
The team made use of Dataiku starting with initial exploratory data analysis (EDA), for python coding and use of custom modules, through to production deployment and BAU operation including model performance monitoring, data monitoring, and resource monitoring.
The end-to-end MLOps process in Dataiku is streamlined, integrated, and easy to use. Specifically, Dataiku has enabled us to easily manage the following key aspects of the MLOps workflow:
Business Area: Supply-chain/Supplier Management/Service Delivery
Use Case Stage: In Production
This project is highly innovative, novel, and transformative within Australia Post, as it is bringing real-time forecasts to users at our facilities, thus enabling a level of real-time and data-driven decision-making that has not been possible to date.
In short, operational decisions can now be made in a timely manner as required by the time-constrained daily cycle of our network operation teams.
Most importantly, these forecasts are relevant, actionable, accurate, and highly automated through the use of the Dataiku platform.
From a scale and technical point of view, the forecast generation process is now streamlined end-to-end in Dataiku, and can easily be scaled out to more facilities nationally.
Specific business metrics of success include:
The specific value brought by the Dataiku platform and the Dataiku team include:
Value Range: Dozens of millions of $