探討建置時尚服飾業C2B精準資料庫之研究

Abstract

全球時尚流行業一直是備受關注之產業，然而在疫情浪潮對巨頭企業的撲滅之下，更加彰顯了時尚產業來到轉型的契機點 ，時尚企業大幅度擴張、生產 ，彈性又快速的設計能力 雖是其優勢 ，卻疊高商品庫存長期導致財務負成長 。而現今 除了全通路的轉型、環保的訴求 、訂閱或租用的新促銷手段 ，是否有更根本的解決方案呢 為解決三大痛點： 市場預測不夠精準導致高居不下的庫存成本 、 難以整合的設計師美感與消費者實際需求 、 多樣化生產與庫存導致資源棄置之環境汙染 本研究 從流行商品的「產品力」為核心切入點，欲探討現有服飾設計流程針對時尚流行預測之痛點，並試圖解構出可標準化之模組， 期待輔佐以大數據分析工具的成熟技術，能發展出有助於時尚產業轉型之機制。本研究聚焦於探討三項核心研究問題： 當前服飾產業技術或經營之痛點為何？如何精準掌握消費者對服裝之需求？如何藉由資訊科技提升設計師對市場之預測力？ 並依文獻與現行平台資料分析，先探討整體服飾經營模式 痛點與現有 資 料庫工具， 再 以 流行設計師為 深入訪談 研究對象， 交叉 比對證明長期以來 B2C的資訊蒐集及匯整的模式，再也不適用快時尚或疫情後重新定義慢時尚的經營模式。本研究的結果發現，將服飾設計的資訊來源模式變更為 C2B將能大幅解決痛點。當前 無論何種商業模式 （大量製造或少量訂製 ），設計師的設計能力都是最重要的銷售關鍵，否則便必須針對庫存進行各種讓利促銷 ；而 當前業界設計流程較無標準化、台灣市場也較少使用數據庫，多以設計師個人經驗為主 總結 經過 C2B平台 大量資料貼標分類後， 資料庫Tagging最重要的變數包含流行色系、整體搭配、 材質預測、風格元素，以及數位打版之視覺呈現。以此為架構之資料庫呈現，將能大幅降低設計時間 預測台灣市場未來 3至 6個月內的流行時尚， 達到快速生產及降底庫存的效益 。The Fashion industry had always attracted global attention, especially when the leading companies and brands declined sharply and faded away after the attack of COVID-19. Popular news and headlines had demonstrated the critical position of fashion industry—risks, or opportunities. Data has shown how fashion companies were ruined by their inventory and unfavorable financial structure after ambitious expansion and diversified production. Was there any fundamental solution instead of building Omni-Channel, claiming environmental protection, or new promotion methods like subscription and rental?According to the industrial data and literature review, three major pain points of Fashion Industry are discussed: the high inventory cost caused by the inaccurate market forecast, the difficult integration of designer aesthetics and the actual needs of consumers, and the environmental pollution caused by resource disposal after diversified production. Thus, this study focuses on three core research questions: What are the current technical or operational pain points in the apparel industry? How to reach consumer fashion demand precisely? How to rely on information technology to enhance designers’ ability to predict market? This study explores design processed for fashion prediction, trying to deconstruct a standardizable mechanism.Popular designers were invited to the in-depth interviews. The results demonstrated that changing the information source mode of clothing design to C2B would greatly solve the pain points. In fact, all kind of fashion business model (mass manufacturing or small-scale customization) is facing sales problems generated by designers’ forecast ability. Besides, design processes in the current industry are relatively unstandardized in Taiwan market. Hence, modeling and automated data analysis could be helpful. Necessary C2B variables of Database Tagging include popular color, material prediction, style elements, and digital pattern visualizing. The database based on classified consumer data could predict the fashion trends in the Taiwan market in the next 3 to 6 months, and achieve the benefits of rapid production and lower inventory.