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Face the process yield rate improvements of motherboard, although general enterprises finish deployment goal of each functions by overall quality managements, through quality improvement methods, industry engineering methods, plan‐do‐check‐act (PDCA) methods and other improvement solutions, but it is only can be improved partially and unable to enhance the yield rate of product to the target. It only can takes one step ahead to enhance the process yield rate of motherboard with six sigma (6 σ) overall DMAIC process and tactics. This research aimed to use six sigma quality improvement tactics by DMAIC systematic procedure and tactics, and find the key factors that effect to the process yield rate of surface mount technology. It also identified the keys input and process and output index to satisfy customer requirements and internal process index. The results showed that the major effective factors by fishbone and process failure modes and effects analysis (PFMEA). If the index of input and output that can be quantified, the optimum parameter can be found through design of experiment to ensure that the process is stable. If the factor of input and output that cannot be quantified, we found out the effective countermeasure by Mind_Mapping, make sure whole processes can be controlled stably, to reach the high product quality and enhance the customer satisfaction.

The definition of cycle time is the time from the wafer start to the wafer output. It usually takes one or two months to get the product since customer decides to produce it. The cycle time is a critical factor for customer satisfaction because it represents the response time to the market. Long cycle time reflects the ineffective investment for the capital. The cycle time is very important for foundry because long cycle time will cause customer unsatisfied and the order loss. Consequently, all of the foundries put lots of human source in the cycle time improvement. Usually, we make decisions based on the experience in the cycle time management. We have no mechanism or theory for cycle time management. We do work‐in‐process (WIP) management based on turn rate and standard WIP (STD WIP) set by experiences. But the experience didn’t mean the optimal solution, when the situation changed, the cycle time or the standard WIP will also be changed. The experience will not always be applicable. If we only have the experience and no mechanism, management will not be work out. After interview several foundry fab managers, all of the fab can’t reflect the situation. That is, all of them will have an impact period after product mix or utilization varied. In this study, we want to develop a formula for standard WIP and use statistical process control (SPC) concept to set WIP upper/lower limit level. When WIP exceed the limit level, it will trigger action plans to compensate WIP Profile. If WIP Profile balances, we don’t need too much WIP. So WIP level could be reduced and cycle time also could be reduced.

Due‐date performance (DDP) is a very important performance indicator for the companies. Thus, companies with a high hit rate would have greater competitive advantage; on the contrary, companies that delay customers' orders frequently would lose sales opportunities and reputations. Therefore, there were many academic studies and practical efforts to improve DDP in the past, but the problem of low hit rate still exists. In order to increase the hit rate, some companies have focused on reducing the variation, while others focus on production management, but is the real problem affecting the low rate variability or production management? This is indeed difficult to be validated through practice. Therefore, this study designed three scenarios, tested each scenario for 30 times, each test involved seven subjects. The tests were to provide counter‐evidence in the Job Shop environment without variation. If the variation is the main factor of affecting hit rate, the rate at this time should be good; otherwise, the assumption that variation is the main cause is rebutted. The results demonstrated that production management planning is the main cause, and the method of enhancing the hit rate is obtained during the test.

The purpose of this paper is to propose a mobile and social-based question-and-answer (Q&A) system that analyzes users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers.

This research applies first-order logic (FOL) inference calculation to generate question/interest ID that combines a users' social information, interests and social network intimacy to choose the nodes that can provide high-quality answers. After receiving a question, a friend can answer it, forward it to their friends according to the number of TTL (Time-to-Live) hops, or send the answer directly to the server. This research collected data from the TripAdvisor.com website and uses it for the experiment. The authors also collected previously answered questions from TripAdvisor.com; thus, subsequent answers could be forwarded to a centralized server to improve the overall performance.

The authors have first noticed that even though the proposed system is decentralized, it can still accurately identify the appropriate respondents to provide high-quality answers. In addition, since this system can easily identify the best answerers, there is no need to implement broadcasting, thus reducing the overall execution time and network bandwidth required. Moreover, this system allows users to accurately and quickly obtain high-quality answers after comparing and calculating interest IDs. The system also encourages frequent communication and interaction among users. Lastly, the experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.

This paper proposes a mobile and social-based Q&A system that applies FOL inference calculation to analyze users' social relationships and past answering behavior, considers users' interest similarity and answer quality to infer suitable respondents and forwards the questions to users that are willing to give high quality answers. The experiments demonstrate that this system achieves high accuracy, high recall rate, low overhead, low forwarding cost and low response rate in all scenarios.