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The issue, forecast preparation of elements in routine maintenance, is to predict daily consumptions of the shared item groups within laptops. Maintenance records of applied materials and sales records of products would be used as the base to predict future consumptions. Considering the time spent from purchasing materials globally to delivering materials to maintenance base, we have daily forecast preparation offer to predict the weekly consumptions in the next 21 weeks, which enalbes customer service unit to purchase and operate transportations of elements. How to predict the consumption precisely has become a vital issue to lower the cost. This issue is for acedemia to participate only, and are welcome to lower the preparation costs by exploring future consumptions of shared items through data science.This is an open-ended problem that can be solved consistently over a long period of time, and has offered milestone awards for participants. the award will be given to the first three participants who reached the Baseline(MAPE<40), and Private Leaderboard will be updated occasionally.After the milestone is ended, date set will be updated(around October), and the scores (includes individual ones and the ranking) will be cleared.Prize InformationMAPE < 20 300,000 discount points of hicloudMAPE < 30 200,000 discount points of hicloudMAPE < 40 100,000 discount points of hicloudRewards were provided by Chunghwa TelecomNote: 1.Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically.Note: 2.Chunghwa Telecom deserves the right to make changes to the terms and conditions herein.Note: 3.For those who reached the baseline, the awards and certificates will be given after the models passed the verification.The example of calculating points can be referred to https://aidea-web.tw/computing
「維修元件備料預測」是對各共用料件群組進行長時間的耗用量預測議題，使用料件維修歷史紀錄以及產品銷售紀錄做為未來耗用量的預測基礎。因為產品銷售後，客服單位必須備足維修用料，以提供消費者產品保固及保修期間內的維修換料服務。然而料件可能在產品銷售生命週期中任何時段發生停產，如何精準的一次性採購長時間的維修料件用量，成為重要的成本改善問題。本議題僅限學界參與，歡迎學界菁英透過數據科學來探索共用料件的未來耗用量藉以降低備料成本。
