高科技廠房設施委員會調查智慧廠房研發需求，於110年成立A1〜A3三個研發小組，導出11個研發議題。智慧廠房之數位轉型有其不確定性，近年在Covid-19下，研發討論只能線上，在不確定及模糊情況下，如何順利執行研發專案值得探討。
本研究敏捷管理與不確定性&模糊性(uncertainty and equivocality, U&E)對智慧廠房研發專案之影響，提出敏捷因子用在研發管理上，調查研發之成功與敏捷因子運用程度，分析其效果；另分析研發議題之U&E，建議研發如何掌握U&E，以有效管理研發專案。
本研究採用試探性個案研究法，問卷收集3個研發小組個案資料，於前3次問卷調查導出研發需求議題，探討業主與供應商的共同需求及議題的演變，後2次問卷調查研發工作之U&E，了解其與敏捷因子互動，及對研發工作的影響。
研究結果顯示，研發小組在敏捷4個因子的運用皆在中上程度，溝通有效性(2.39)及成員了解議題(2.38)較高。成功與敏捷因子相關性結果顯示，當小組成員了解議題、溝通有效性的程度越高，顯著提升過程(顧客需求1、市場機會0.96)與結果(成本效益1、供應商適用1)的成效，研發小組應多以資料分享、定期溝通來降低業主與供應商間的資訊落差。
研發議題的U&E最高在作業相依性(3.6)，表示議題研發的介面較複雜，需多增加資訊及有效的溝通。研發議題的不確定性(13.7)明顯多於模糊性(10.3)，表示整體議題研發資訊收集與分析的需求，多於問題的定義與解釋。U&E與成功因子的相關性，可看出無論在成功的過程、結果因子，相關程度較高多在模糊性(與市場機會0.92，未來成果明確性1)，意指研發專案應以模糊性為優先考量，選擇適當的協調方式。
本研究導出敏捷與U&E對研發專案之影響，未來可建立敏捷方法於實務上的操作，同時診斷議題之U&E，於研發期間持續規劃、執行、反饋。

Abstract
The influence of agile management and U&E on smart factory R&D projects
Min-Chen Hsu
Prof. Andrew S. Chang
Department of civil Engineering
National Cheng Kung University

INTRODUCTION
In recent years, as digital transformation challenges approach, collaborative R&D and coordinated cooperation between organizations become more important and traditional project management method would be insufficient. Some literature shows that agile management methods can assist in responding to uncertain and ambiguous environment (Dybå et al., 2014; Aldahmash et al., 2017). R&D has requirments to be fulfilled in the era of digital transformation in smart factories, especially in the Covid-19 pandemic. R&D projects have uncertainty and equivocality (U&E), it’s also worth exploring that how to implement R&D projects effectively.
This research proposed using agile management in R&D projects, investigated the use of success and agile factors, and analyzed their effects. In order to manage R&D projects effectively, it also analyzed the U&E of R&D projects and propose suggestions of dealing with the U&E.
MAERIALS AND METHODS
This research used exploratory case study method to observe and assist the R&D projects of HTFC. With its study teams A1~A3 and their 11 projects being case studies. A1 is to utilize AI, IoT, Big Data, 5G in air abatement system, chiller systems, and heating, ventilation and air conditioning; A2 is data collection, transmission, application of AI, data needs, and maintenance of databases; A3 is sensors installation and detection.
The case information was collected from the three teams and five times of questionnaires. It investigated the R&D issues from the first three questionnaires to explore the common needs of owners and suppliers and observe the evolution of the projects; it used the last two questionnaires to survey the U&E of R&D projects, to understand their interaction with the agile factors and impact on R&D projects. Furthermore, it analyzed the collected data, calculated the correlation between success factors, agile factors and U&E, and put forward explanations and suggestions for the development of projects eventually.
RESULTS AND DISSCUSION
The results showed that the study teams used four agility factors to a moderate-upper level, especially higher in effectiveness of communication (2.39) and members understanding the issues (2.38). The correlations between success and agile factors indicate that when group members understand more about issues and have higher effectiveness of communication, they would significantly increase the effect of their process (customer needs, 1, market opportunities, 0.96) and consequence (cost effective, 1, applicability to the suppliers, 1). Hence, study teams should reduce the gap between owners and suppliers by sharing information and communicating regularly.
For U&E, the R&D projects had the highest U&E in operational dependency (3.6), indicating that the interface of cooperation is quite complicated, so they need more information sharing and effective communication. Uncertainty (13.7) of R&D issues is significantly higher than equivocality (10.3), indicating that the R&D information collection and analysis need is more than the problem definition and interpretation. On the other hand, the results of correlation between U&E and the success factor show that, regardless of the success factor of process and consequence, the level of correlation is higher in equivocality (with market opportunity 0.92 and clarity of future 1), which means that a R&D project should choose an appropriate coordination method based on their equivocality. When equivocality is high, face-to-face communication should be more than paperwork; when equivocality is low, it can be replaced by written communication to reduce unnecessary cost.
CONCLUSION
This study derives the influence of Agile and U&E on R&D projects. When the study teams keep taking data sharing and regular communication as their goals, it can reduce the gap of information between owners and suppliers to raise the probability of success. In addition, in the U&E of issues, the R&D projects have much more influence from equivocality. So equivocality should be taken as the main consideration in the coordination operation. It can establish agile methods in practice, diagnose the U&E, continue to plan, execute and feedback during R&D in the future.

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