車規電子新產品規劃決策分析之研究-以 IATF16949 先期產品品質規劃程序(APQP)及 AEC-Q 驗證標準為主

Abstract

車規國際標準要求之新產品規畫設計與驗證核准程序頗為嚴謹。全新或較複雜的新產品驗證失敗率可能高達50%以上，常須設計變更及額外時程以解決失效問題，因而延宕上市時程(Time to market)，淪為次順位供應商或失去商機，研發費用淪為沉沒成本。故，不乏隱瞞甚或造假以保有商機，而引發重大事故，大量召回甚至醜聞，問題存在已久，且在過去十餘年快速惡化。 2015年9月，福斯集團柴油排放造假驚動全球後，截至2020年之罰鍰、賠償和解等已燒掉300億歐元（台幣1兆多）。2017年6月，高田因氣囊門事件向東京地院提交破產時負債達百億美元，創日本製造業紀錄。高田在2004年新產品驗證時就已發現產品設計缺陷，從未面對解決。其後，汽車業造假醜聞及召回升高不斷，至2024年爆發日系車廠造假醜聞，連豐田集團亦淪陷，子公司大發等遭發現測試數據造假達30年，亦為新產品驗證時隱藏，擴大調查後發現更多造假，雖在同年7月受日本國土交通省緩頰背書落幕，也應證了造假陋行及本研究重要性。 深邃複雜的電子半導體元件及製程設計須諸多整合，才能完成高品質及具競爭力之車規新產品。新產品規劃相關策略及決策因子多又複雜，但，高度工程導向的B2B半導體業，至今仍僅採用簡單之策略觀念及決策方法，管理與工程間寬深的鴻溝尚難以跨越，新產品之驗證失敗風險缺乏客觀衡量，仍賴錯誤後修正，即使見樹也難見林，衍生上市時程壓力下因驗證缺陷失效所引發的隱藏或造假風險。本研究創建了車規電子新產品規畫決策樹分析雛型模型「C-LIN」納入了10項在新產品之規劃策略，決策與執行過程中之重要決策因子，可顯著影響新產品驗證失效之風險及影響程度，並以三種機器學習模型方法對高達64萬8千筆模擬資料集進行訓練及驗證，分析及展示「C-LIN」模型之可行性及簡單敏感性分析確認一致性，輔以價值主張圖說明可解決之痛點，可協助量化分級及降低驗證失敗風險及其影響，據以提昇車規電子新產品規劃之決策品質，最大化新產品上市時程達成率及營收市佔率，配合持續改善閉循環，有助於跨越暨存鴻溝，開啟此議題之創新性研究方向。

Automotive-grade product planning, design, and validation procedures are highly rigorous by international standards. New or complex products can experience verification failure rates exceeding 50%, often necessitating design changes and schedule extensions to address defects. Such delays jeopardize time-to-market, demote suppliers to lower priority, and turn R&D investments into sunk costs. To avoid lost business, some firms conceal or falsify test results—practices that have led to major recalls and reputational scandals, worsening dramatically over the past decade. Following the 2015 Volkswagen diesel emissions scandal-which has cost over €30 billion (NTD>1trillion) in fines and settlements by 2020-and Takata’s 2017 airbag crisis, where latent defects discovered in 2004 went unremedied, the industry saw recurring failures culminating in a 2024 data-falsification scandal among Japanese OEMs, including Toyota subsidiaries. These incidents underscore the urgent need for transparent, quantitative risk assessment in new-product planning, decision-making and validation. This study introduces “C-LIN” a prototype decision-tree analysis model for automotive- grade semiconductor product planning. Using three machine-learning approaches, we demonstrate its feasibility and perform a simple sensitivity analysis to confirm consistency. Coupled with a value-proposition map, “C-LIN” helps quantify and tier verification‐failure risk, maximizing on-time product launches, revenue, and market share while closing the engineering-management gap through a continuous improvement loop.