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Objective: Develop an Minimum Viable Product to automate the definition of spare parts needs for Schneider's energy systems, based on client installations, usage conditions, and the lifecycle phase of the systems.
Context: Schneider Electric aims to optimize the management of spare parts for its energy systems by automating the needs definition process. Currently, this process is manual and relies on estimates based on experience and maintenance history. The goal is to develop a machine learning algorithm capable of accurately and proactively predicting spare parts needs.
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