YouThought Corporation was established in 2010 by Dr. David Kuo who graduated from Industrial Engineering and Engineering Management Department at National Tsinghua University。Dr. Kuo’s research had won the best paper award from IEEE transaction on automation science and engineering in 2012.
The core competence of YouThought is to explore opportunities from huge factory data given the complicated factory situations. We incorporate various approaches, including statistics, data mining machine learning, operation research and information technology, to make your factory operation more intelligent and more efficient.
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YouThought Corporation was established in 2010 by Dr. David Kuo who graduated from Industrial Engineering and Engineering Management Department at National Tsinghua University.Dr. Kuo’s research had won the best paper award from IEEE transaction on automation science and engineering in 2012.
The core competence of YouThought is to explore opportunities from huge factory data given the complicated factory situations. We incorporate various approaches, including statistics, data mining machine learning, operation research and information technology, to make your factory operation more intelligent and more efficient.
Industry 3.0 applies automation and information technology to substitute the jobs human beings are not willing to do. While, industry 4.0 aims to do jobs better than human beings. Big data and artificial intelligence are critical for success of intelligent factory in industry 4.0. YouThought has being focusing on application of big data and AI on high-tech manufacturing and to create solutions different from customers and competitors.
Intelligent manufacturing is the core for industry 4.0. Our solutions fill the gap for the insufficient parts in big data and neural system, respectively.
u-Optimizer makes use of AI to find out the optimal production schedule to maximize productivity.
While u-Efficiency apply big data to discover the root causes for machine idle time and efficiency loss.
Combine the big data & AI of U efficiency and u optimizer, YouThought develop u-Planning to solve the planning issues.
The big data & AI expert to enable operational intelligence.
Besides software solution, YouThought provides consulting service to analyze tremendous machine data log collected by IoT and enhance machine productivity.
Including the founder, our senior executives have Ph.D. and Master degrees in industrial engineering and information engineering. At TSMC or other high-tech industry companies, they got more than 20 years’ experience in manufacturing management and software systems development.
工業4.0 浪潮襲捲全球，「智慧製造」成為產業革新重點，期能在工廠效率、良率、產出與成本各方面獲得新的突破。台灣PCB產業在全球市占第一，為了維持競爭優勢，很多PCB業者正在布局工業4.0與智慧製造。目前多數PCB廠的智慧化程度，僅處於工業2.0至2.5之間，許多工廠雖然具備自動化設備與 MES，但仍未善用既有系統所掌握的巨大資料，甚至為了搶搭工業4.0 風潮，貿然大舉投資 IT 與 IoT軟硬體，裝設許多 IoT感知器及自動化系統，收集更多巨量的資訊，卻沒能事先想好怎麼將這 些資料轉化為有價值的決策資訊，因此無法有效達到提升良率、增加生產力、準時達交、縮短生產週期時間等目標。
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當市場邁入少量多樣，導致產品生命週期縮短，製造業卻又面臨人力短缺、訓練成本節節上升等製造型態的轉變及挑戰之際，智慧製造便成為製造業轉型升級的必要趨勢，包括工業物聯網(IIoT)、大數據分析、雲端應用、人工智慧(AI)等相關技術，都是近幾年來智慧製造的關鍵項目，如何加以整合及應用，如將AI及IIoT整合為AIoT，更是提升製造產業實力的關鍵。 由科技部新竹科學工業園區管理局及DIGITIMES共同主辦的「新智造時代」論壇，因此邀集台灣科研、製造以及新創業者集聚一堂，從全球市場的機會、智慧製造的趨勢到如何落實本地AIoT發展行動方案，打造台灣新智造時代的競爭力。
Read MoreThe big data & AI expert to enable operational intelligence.
大數據(Big data)近年來快速成長，根據麥肯錫全球研究中心在2011年5月發表的全球大數據研究報告指出，全球資料量光是在2010年就增加了70億GB，相當於4千座美國國會圖書館典藏資料的總和。
如何產生、消費和儲存大數據，已經成為近年來企業IT應用的重要趨勢。如在eBay上，平均每天有將近1億名用戶查詢商品數百萬次，更有上百萬件商品在線上交易，導致eBay資料庫每天新增的資料，超過1.5兆筆，每天增加的資料量超過50TB，這些大數據如果沒有作進一步的分析應用，勢必會影響eBay的企業營運。
宇清數位智慧(Youthought)股份有限公司銷售暨服務副總經理涂耀仁先生，針對「大數據(Big Data)開啟新一代智慧工廠」做主題分享。他先介紹該公司，主要專精於Big Data(大數據)與Data mining(資料探勘)的研究，開發出對工廠的生產力、生產週期與成本等進行有效改善的「智慧分析系統」。管理團隊來自台積電、旺宏等台灣製造業大廠，其研究亦獲得2011年IEEE自動化科學與工程學匯刊的最佳論文獎。
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隨著電腦技術發展，資料的存儲量成倍增長，而大量資料分析方法的發展，卻難以望其項背。資料採礦能從大量的資料中發現潛在規律，提取有用知識的方法和技術，不僅能分析問題，也能預測未來趨勢。
宇清數位智慧總經理郭仲仁表示，該公司主要產品為智慧分析系統、生產力提升顧問、資料價值發展服務，透過資料採礦，由電腦技術、人工智慧技術、統計技術等，運用資料分析方法、關聯分析、決策樹等技術及連結雲端運算，從大量資料中挖掘出隱含的、先前未知的、對決策有潛在價值的關係、模式和趨勢，並用這些知識和規則建立，用於決策支援的模型，提供預測性決策支援的方法、工具和過程。
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We will arrange experts to visit you according to your needs, and will provide you solution plan and detailed product descriptions.
Based on YouThought’s confidence in its own solutions, we welcome customers to verify the software through POC (Proof of concept).
In general, the u-Efficiency/u-Planning POC takes 4 to 6 weeks, and the u-Optimizer POC takes 6 to 10 weeks to conduct the POC . The customer can see the critical results on the system, and he definitely will feel the conspicuous value brought from YouThought’s solutions.
In the stage of system importation, YouThought will assign experts who have rich experience in factory management and system to fully communicate with customers. On the one hand, we will provide customers with necessary training on system-related knowledge and specifications. On the other hand, we catch the requirements from customers. In general, customers can use the solutions on their own 6 to 8 weeks since go-live of the solutions.
For each factory that imports YouThought’s solution, we will assign a dedicated customer service staff to answer questions about operations of the software, and provide consulting services for KPI improvement and system parameter setting, etc.
The general big data analysis solutions provide a large number of statistical and data exploration tools, they allow customers to extract valuable information from huge amounts of data. Therefore, they require a lot of software, hardware, and manpower to build big data infrastructure. Unlike general solutions, after more than 10 years of research by YouThought RD team, u-Efficiency integrates academic theory and factory domain experience to define 32 KPIs that will affect plant efficiency, thus we simplified the process of extracting big data. Hence, customers can adopt u-Efficiency to possess big data analysis capability even they has no big data infrastructure.
Taking a photo machine group for example, assuming that there are 5 machines in the group and 10 lots to be processed in the next 2 hours, the permutations and combinations are more than 5th power of 10. The complexity of the problems has far surpassed the dispatching rules maintained by engineers. If the customer already has a dispatching system, the optimization problem with high complexity is handled by u-Optimizer. The optimization result is then assigned to the dispatch system. The dispatch system rule will be greatly simplified. If the customer does not have a dispatch system yet, it is still suitable for importing u-Optimizer. The main reason is that the role of dispatching system is to follow the lot sequence defined by u-Scheduling and to correct the sequence according to the situation change. Due to the fact that the method of dispatching is already very simple, it is even possible for customers to develop their own dispatching system and do not need to purchase it.
