建構策略性生產能力之估計方法

Abstract

在高科技產業的經營策略中，生產設施的產能投資規模是一項極重要的議題。 產能是一策略性的長期投資，牽涉到各種不同複雜因素和極為龐大的投資金額，不僅需 能滿足未來市場需求，且建置後的使用期限長，缺乏變動彈性，可能嚴重影響公司財 務表現以至於生存能力。 本計畫為多年期研究計畫之部分，整體研究的終極目標是發展一決策輔助工具，從 產能供給面的角度幫助決策者做長期產能投資規劃的全球佈局運籌管理；特別著重在由 產能供給的角度提供單一生產設施的可能生產能力之有效資訊，包含投入資源和可獲得 產出的估計，以彌補過去客觀資訊不足的困境。更具體地說，整體研究計畫的主要目的 在藉過去經驗或同業相關資料瞭解和估計理想狀況下的生產能力，特別是在規模上的變 化型態，以獲得設施產能特性資訊，幫助企業做長期策略產能投資規劃決策。 整體研究將以生產經濟學中的確定性(deterministic)生產前緣模型(frontier models)為 學理基礎，並進一步考量資料的隨機抽樣誤差，以及非凸(non-convexity)之生產技術， 分三大主題加以探討，分別為「大樣本情境下之生產能力前緣型態分析」，「小樣本情境 下之生產能力前緣型態分析」和「生產能力前緣型態資訊整合與呈現」。延續上一年度(目 前正在執行中)所發展「大樣本情境下之生產能力前緣型態分析」的成果，接續的研究 工作首先將著重在以有限樣本進行生產能力前緣型態的統計推論，接著並將所得的結果 加以視覺化(visualize)，使得由受測資料所構成的前緣可利用簡單圖形表達。本研究所建 構的策略性生產能力之估計方法除了產業實務有所貢獻之外，在學理上更是新的突破， 能做為樣本資料凸性假設之統計檢定。

High-tech industry, such as semiconductor and TFT-LCD panel manufacturing, significantly contributes to Taiwan's economy nowadays. Determining proper global production capacity is an important issue in today’s competitive business environment. Capacity investment decisions are long-term strategic and involve with huge amount of investment and many different intangible factors. The decision not only faces future demand uncertainty, and lacks for flexibility to adjust once decision is made. The decisions thus not only have impact on profits and market share, but also the sustainability of the firm. This study is the second and third parts of a multi-year project. The ultimate goal of the overall project is to develop a decision aid for strategic production capacity investment, from the supply perspective, by providing proper objective capacity information, particularly the scale performance of a single facility. Technically speaking, we estimate the production capacity according to the data from the past or industry peers. Applying deterministic frontier models, our study will further consider randomness of the data and non-convex production processes. Three subjects, (1) production capacity estimation with infinite sample, (2) production capacity estimation with finite small sample, and (3) information integration and vitalization on production capacity, are investigated. Extending the results of the first subject, the study will focus on the later two subjects to develop a statistical inference model that analyzes the shape of the capability frontier, and to visualize the inference results. If concluded successfully, a tool will provide more objective and suitable information for strategic capacity investment decision making; new knowledge will also contribute to the academia literature.