以藍海策略觀點結合品質機能展開法之資源分配決策模型

A Model of Resource Allocation Problem Solving with Blue Ocean Strategy and Quality Function Deployment

陳寧世(Ning-shih Chen)

指導教授 ： 廖本哲 ； 邱榆淨

中原大學/商學院/企業管理研究所/碩士(2012年)

https://doi.org/10.6840/cycu201200159

摘要

當顧客的需求被成功地開發出來以後，在初期通常是需求大於供給的情況，並且散發著豐厚的利潤與廣大市場的誘人氣味，吸引著競爭者進入，掠食這塊豐饒的市場。而隨著競爭者的進入，供給與需求產生了變化，在短暫的供需平衡後，最終可能進入到供給大於需求的紅海市場，而這個週期也持續在縮短。另外，由於全球化的關係，雖然為企業帶來廣大的市場與商機，但原本地區性的競爭，同時也轉變成為全球性的競爭，背後伴隨一個非常大，且不得不面對的競爭壓力。而隨著競爭者增加，供需產生變化，企業也從生產製造為主軸的「產品導向」，轉變成為以顧客需求滿足之「顧客導向」，企圖在競爭的市場中脫穎而出，超越競爭對手。並且，產品已不再只是狹義的實體產品，產品是滿足需求的價值傳遞過程，可能是服務，也可能同時包含兩者。然而，顧客的需求是多樣的，也會隨著時間與環境改變，而需求被滿足與否卻又是顧客主觀的感受。因此，需要以科學的方法找到顧客的需求，同時產生競爭策略與行動，快速有效地配置組織有限的資源。
品質機能展開法就是其中一種顧客導向的方法，焦點在顧客的需求確認與轉換，由顧客需求的滿足為出發點，經過一系列的轉換程序展開至產品或服務樣貌的過程，以決定資源分配。而藍海策略也是一個顧客導向的哲學，聚焦在顧客與非顧客的價值上，考量企業可運用的資源，透過不侷限的創新思維，去尋求能帶給顧客與企業價值的豐饒市場，同時降低成本以提高潛在競爭者進入的藩蘺，而擺脫無謂的競爭。
然而，品質機能展開法由顧客的需求為起點，配適企業的資源，一層一層地向下展開，產生產品具體樣貌與操作的定義，但卻缺乏一個具有策略的方向。而藍海策略站在至高點綜觀全局，提供前進的大方向與原則，卻缺乏向下展開到操作面的工具。因此，本研究將結合藍海策略與品質機能展開法，各取其優點，並且加入狩野模式的品質要素概念，建立一個具有策略方向，且以最大顧客滿足為目標的顧客導向數學模型。最後再以一個實例，用線性規劃及基因演算法的方法求解，求解結果證明本研究所建立的數學模型，兩種求解方法皆可得到具有策略方向與操作定義之資源配置決策建議。使用線性規劃求解時，由於限制條件的關係而無可行解空間，在放寛限制條件後可得到最佳解；而以基因演算法求解，以最貼近藍海策略的價值曲線為適應性評估函數，能夠快速且有效地獲得數個最適解。

關鍵字

線性規劃 ； 基因演算法 ； 藍海策略 ； 品質機能展開 ； 狩野模式

並列摘要

Once the customer demand is successfully identified, the potential market, which is full of interests and incentives, will attract more rivals to compete during the initial stage where the demand is much greater than supply. Then there would be a change between supply and demand since the market is competitive and the supply will be much greater than the demand. In addition, the globalization brings enterprises more opportunities and competitive pressures. In accordance with more rivals entering into the market, the business has been modified from “product oriented” to “customer oriented”, in order to compete with their rivals. Moreover, the product has even more meanings not only on product itself, but its related services. However, the customer demand is multiplex which will be changed with the time and environment; hence, the demand satisfaction will depend on customer’s perception. The customer demands have to be defined by scientific measures, so that the competitive strategy and course of action could be merged to effectively equip organization with limited resources.
Quality Function Deployment (QFD) is one of the customer-oriented approaches with a specific focus on the demand definition and transfer. QFD begins with the customer satisfaction and goes through a series of deploying processes to products or services in which the resource distribution is defined. Blue Ocean Strategy, as well, is a customer-oriented philosophy which not only focuses on the customer and non-customer values, but also considers business available resources. Blue Ocean Strategy also adopts the unconstrained innovative thinking and seeking a fruitful market to benefit customer and business value, so that the cost is reduced and the competitive advantage is dramatically increased.
Starting point of Quality Function Deployment is customer demand and the resources are thus allocated, but it lacks strategic direction. Blue Ocean Strategy provides principles to overall aspects, and needs to cooperate with Quality Function Deployment. This research tends to combine both approaches with concepts of Kano model’s quality attribute in order to set up a strategic mathematical model which could maximize the customer satisfaction in the end. The proposed model was examined and compared to both “linear programming” and “genetic algorithm” measures which indeed give the same results to strategy and resource distribution. The research also found no results to “linear programming” approach due to the conditional requirements. Instead, the “genetic algorithm” approach founds more adaptable solutions and proves it is consistent with the form of expected value curve of Blue Ocean Strategy.

並列關鍵字

Quality Function Deployment (QFD) ； Blue Ocean Strategy ； Genetic Algorithm (GA) ； Linear Programming ； Kano model

參考文獻

Barney, Jay B. (2002). “Gaining and Sustaining Competitive Advantage,” 2nd ed., Prentice Hall, New Jersey.

Blau, Peter M. (1964). “Exchange and Power in social life,” Wiley, New York.

Chan, Lai-Kow and Ming-Lu Wu (2002). “Quality Function Deployment: a literature review,” European Journal of Operational Research, 143(3), pp.463-497.

Chang, Pei-Chann, Jih-Chang Hsieh, and Chen-Hung Hsiao (2002). “Application of Genetic Algorithm to the Unrelated Parallel Machine Scheduling Problem,” Journal of the Chinese Institute of Industrial Engineers, 19(2), pp.79-95.

Chang, Pei-Chann, Jih-Chang Hsieh, and Yen-Wen Wang (2003). “Genetic Algorithms Applied in BOPP Film Scheduling Problems: Minimizing Total Absolute Deviation and Setup Times,” Applied Soft Computing, 3(2), pp. 139-148.