Bidding Process Reinforcement and Price Inference: An Application Case of PCBA-OEM Project

• Main Authors: Wan-Yu Chen, 陳婉玉
• Other Authors: Chih-Min Fan
• Format: Others
• Language: en_US
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/24842832894558109739

碩士 === 元智大學 === 工業工程與管理學系 === 98 === Abstract With the intensified pressure for cost-efficiency in electronics industry, OBM (Original Brand Manufacturer) turns into inviting bids for manufacturing of new project development. Bidding invitation is an approach to discern a lowest cost from a range of bids submitted by bidders. For a bidder, how to improve the project process effectiveness for promoting successful project rate is a critical issue to be prosperous. Top-down process controlling is the conventional approach to process management which is concentrated on the intra phase process management. Phase is the division of project process by which project stages are defined. Based on top-down approach, the inter-phase dependency is overlooked which might lead to the delay in bidding proposal. On the other hand, bidding price precision is influenced by the implicit bidding knowledge. The knowledge loss of implicit bidding experience results in the poor bidding performance. In this research, Attribute-based bottom-up process reinforcement was introduced to make analysis on current model. Attribute is the essence is the essence of document. Started from document analysis, Critical Contents are generated for process re-phasing. Besides, the objective-aligned Critical Contents are suggested to be pre-acquired for higher project effectiveness. A fuzzy-based bidding price inference model was built to assist price determining based on the predefined IF-THEN rules. IF-THEN rules are abstracted from previous success bidding projects. This inference model solves the problem of bidding knowledge loss. The error rate of discrepancy between actual bidding price and inferred price was limited to +/-5% based on this model. Keywords: PCBA OEM, bidding process reinforcement, document relevancy, bidding effectiveness, knowledge reuse, fuzzy-based bidding price inference