Convenience-retail network scaling operations through data and ML
7-Eleven Australia operates 750+ stores across five states with 9,000 employees and franchisees, serving millions of transactions daily. Their tech stack—anchored in SAP ECC, Azure data infrastructure (Synapse, Data Factory, Data Lake), and emerging ML tooling (Databricks, MLflow)—reveals a company moving beyond transactional retail systems toward predictive analytics. Active hiring is concentrated in operations and data roles, while projects span store expansion, loyalty insights, and ML engine deployment, suggesting they're instrumenting the franchise network to optimize fuel forecasting, category performance, and promotional execution.
7-Eleven Australia is the locally operated subsidiary of the global convenience-retail network, running approximately 750 stores across Victoria, New South Wales, Australian Capital Territory, Queensland, and Western Australia. The franchise model involves over 450 family-owned businesses alongside company-operated locations, employing approximately 9,000 people across both direct and indirect roles. The store network processes an average of seven customer transactions per second. Operations span fuel, food-on-the-go, and fast-moving consumer goods, with a focus on store expansion, supply-chain optimization, and increasingly, data-driven insights for category management and loyalty programs.
Primary systems include SAP ECC for enterprise resource planning, Azure (Synapse, Data Factory, Data Lake, SQL Database) for cloud data infrastructure, Databricks and MLflow for machine learning, Power BI for analytics and reporting, and Workday for HR management.
Current projects include store expansion and relay programs, visual merchandising standards, commercial delivery product optimization, budget-to-execution planning, agile adoption, category financial optimization, digital and loyalty insight programs, and ML engine deployment—indicating a push toward data-driven retail operations.
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