Join us for the next Dataiku Australia and New Zealand User Group event to learn about the Award Winning Machine Learning Use Case!
The global pandemic has accelerated e-commerce growth with more households shopping online than ever before. Whilst Australia Post, a government business enterprise that provides postal services in Australia and is the country’s leading logistics and integrated services business, has a long and proud history to lean on, they continue to face challenges from ever-increasing parcel volumes and a great digital disruption that is shaking up the wider logistics industry.
A key daily activity, at facilities within their logistics network, relates to shift production managers being tasked with making daily resource/staffing planning decisions, that seek to ensure that they 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 point, their 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.
In this session, we will deep dive into the use case which was recognized at the Frontrunner Awards competition at Dataiku with Australia Posts Senior Scientist, James Walter.
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