成衣加工產業為人力密集之產業，在現今世界各國的人力成本不斷上揚，而造成此產業不斷轉換生產基地至人力成本較低之國家，而生產效率也是影響到人力成本的因素之一，因此導入自動化機器及新進技術，也是成衣加工產業未來存活的主要議題。
　　
　　在資訊科技的進步與自動化設備的改進，許多新的自動化機台已投入至成衣工廠的生產線上，除了傳統ERP系統管理外，在MES系統需求下所開發的智能吊掛系統，也投入成衣生產的一環，早期成衣業的MES系統在投入產線時，因為許多因素而造成資料收集不易及導入成本過高，讓許多成衣廠望而卻步。

但現在因各種技術成熟與設備成本的降低，有越來越多的成衣廠開始重視MES系統，近年來更是大量推展智能吊掛系統的引進，來增進生產的效率，智能吊掛系統是結合了物聯網技術的一套生產輔助工具，能改善舊有MES系統資料收集不易及物料運輸的問題。

　　本研究對Ｇ公司所導入推行的智能吊掛系統所收集的生產數據分析，除了與傳統生產方式有重大的改革外，更能即時性反應生產線的效率，進而提供管理者，對生產線的調度能有科學數據及合理性的改善。

Garment processing industry is a labor-intensive industry. In today's world, labor costs increase gradually, causing the industry switch production bases continuously to countries with lower labor costs; and production efficiency is also one of the factors that affects labor costs. Therefore, importing automatic machines and new technologies are significant issues for garment processing industry to survive in the future.

By improving information technology and automatic equipment, many new automatic machines have been put into production lines of garment factories. Additionally, besides traditional ERP system management, intelligent hanging system developed under demand of MES system also put into garment production.
In comparison, when MES system was launched to production lines in early garment industry, data collection was difficult and introduction cost was too high due to many factors, causing many garment factories don't want to implement it at that time.

But due to maturity of various technologies and reduction of equipment costs now, there is a growing and more garment factories have begun to pay attention to MES system. In recent years, the introductions of intelligent hanging systems have been greatly promoted to improve efficiency of production. Intelligent hanging system combines the Internet of Things technology to improve the old MES system which data collection is difficult and has material transportation problems.

In this study, the analysis of production data was collected by the intelligent hanging system by G Company. Besides major reforms of traditional production methods, it can reflect the efficiency of production line more quickly, and provides rational improvements on production line allocation/scheduling with scientific base to managers.

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