High-tech industry的問題，透過圖書和論文來找解法和答案更準確安心。 我們找到下列股價、配息、目標價等股票新聞資訊

Cost-Effectiveness Analysis: A Systems Engineering Perspective

為了解決High-tech industry的問題，作者Eisner, Howard 這樣論述：

Howard Eisner spent 30 years in industry and 24 years in academia. In the former, he was a research engineer, manager, executive (at ORI, Inc. and the Atlantic Research Corporation) and President of two high-tech companies (Intercon Systems and the Atlantic Research Services Company). In academia, h

e was professor of engineering management and a distinguished research professor in the engineering school of the George Washington University (GWU). At GWU, he taught courses in systems engineering, technical enterprises, project management, modulation and noise, and information theory.He has writt

en twelve books that relate to engineering, systems and management. He has also given many lectures and tutorials to professional societies (such as INCOSE - International Council on Systems Engineering), government agencies (such as the DoD, NASA and DOT), and the Osher Lifelong Learning Institute

(OLLI). In 1994, he was given the outstanding achievement award from the GWU Engineering Alumni.Dr. Eisner is a life fellow of the IEEE and a fellow of INCOSE and the New York Academy of Sciences. He is a member of Tau Beta Pi, Eta Kappa Nu, Sigma Xi and Omega Rho, various honor/research societies.

He received a bachelor’s degree (BEE) from the City College of New York (1957), an MS in electrical engineering from Columbia University (1958), and a Doctor of Science degree from the George Washington University (1966). Since 2013, he has served as professor emeritus of engineering management and

a distinguished research professor at the George Washington University.

High-tech industry進入發燒排行的影片

應用多準則決策與模糊積分探討國道客運服務品質

為了解決High-tech industry的問題，作者黃裕倉 這樣論述：

搭乘大眾客運能有效地減少都市交通阻塞及汙染問題，提升客運業者服務品質有助於提高民眾的搭乘意願。過去已有許多相關的研究探討客運服務品質，採用服務品質指標（SQI）結合結構方程模型（SEM）等統計方法，或是使用多準則決策分析方法，但是假設準則間是相互獨立關係。但是從現實社會中來看，許多在不同構面下的評估準則與準則之間可能會存在相互影響關係，以及準則之間的非加法關係，因此本研究將提出一個非加法模型考慮準則間的相互影響，探討大眾客運服務品質。本研究採用計算權重的最佳最差方法（Best Worst Method, BWM），可減少準則之間的比較次數，評估每項服務品質的準則所占整體的權重。再透過模糊積分

（Fuzzy integral）考量評估準則之間的非相加性進行績效整合，並以理想解類似度順序偏好法（Technique for Order Preference by Similarity to an Ideal Solution, TOPSIS）對客運業者的優劣進行排序，並提供管理者改善策略，以有效提升服務品質。本研究以三家台北-宜蘭國道客運公司作為研究對象，研究結果並將與傳統加法行進行比較，探討兩種模型研究結果並提出研究結論。

Equipment Management in the Post-Maintenance Era: Advancing in the Era of Smart Machines

為了解決High-tech industry的問題，作者Peng, Kern 這樣論述：

Recent advancements in information systems and computer technology have led to developments in equipment and robotic technology that have permanently changed the characteristics of manufacturing equipment. Equipment Management in the Post-Maintenance Era: Advancing in the Era of Smart Machines in

troduces a new way of thinking to help high-tech organizations manage an increasingly complex equipment base. It also facilitates the fundamental understanding of equipment management those in traditional industries will need to prepare for the emerging microchip era in equipment. Kern Peng shares i

nsights gained through decades of managing equipment performance. Using a systems model to analyze equipment management, he introduces alternatives in equipment management that are currently gaining momentum in high-tech industries. The book highlights the fundamental internal flaw in maintenance or

ganizational setup, presents new approaches to replace maintenance functional setup, and illustrates a time-tested transformation and implementation process to help transition your organization from the maintenance era to the new post-maintenance era. Fundamentally, it: Breaks down the history of eq

uipment into five phases, Provides a clear understanding of equipment management fundamentals, andIntroduces alternatives in equipment management beyond the mainstream principles of maintenance management.More specifically, the book examines maintenance management logistics, including planning and b

udgeting; training and people development; customer services and management; vendor management; and inventory management. Supplying a comprehensive look at the history of equipment management, it analyzes current maintenance practice and details approaches that can significantly improve the effectiv

eness and efficiency of your equipment management well into the future.This second edition addresses the role of the development of the Internet of Things (IoT) and significant advancements in artificial intelligence (AI) and machine learning (ML) in enabling a new generation of smart machines, whic

h have in turn laid the foundation for Industry 4.0. Equipment utilizing IoT and sensors can monitor components and allow them to be serviced at an exact time without the need for a preventive maintenance schedule. Moreover, equipment replacement rarely occurs at the end of the piece of equipment’s

natural life; rather, replacement is driven by the introduction of new technologies and products, all of which lead to less maintenance activities and reduces the importance of the traditional maintenance function. Maintenance departments today operate with fewer employees and smaller budgets. At a

point when machines are smart enough to keep themselves running or equipment is rendered obsolete by better equipment in a short time, such as with computers and cellphones, companies do not need a maintenance department. This updated edition reiterates the importance of transitioning to the post-ma

intenance era to effectively manage today’s sophisticated, smart yet expensive equipment. Many changes the author predicted a decade ago are accelerating in the IoT era. Equipment management is moving further away from the maintenance era and advancing deeper into the post-maintenance era. The trend

for smart machines is very clear and companies that do not upgrade their equipment will lose their competitiveness. As equipment and factories become smarter, companies must change their practices and organizational structures to manage the new generation of equipment for Industry 4.0.

廣東醫療器械企業協同創新合作夥伴選擇機制與評價體系研究

為了解決High-tech industry的問題，作者蘇旋 這樣論述：

協同創新是創新資源和要素的有效匯集、深度融合的一種創新模式，對創新强化產業戰略的實施具有積極作用。在協同創新中合作夥伴選擇具有先導性和基礎性作用，直接影響協同創新的績效和目標的實現。因此，本文對醫療器械企業主導的協同創新合作夥伴選擇的基本特徵、動力來源、影響因素、評價指標、和評價模型等問題開展研究。本文以界定協同創新合作的内涵為起點，對醫療器械企業協同創新合作夥伴選擇的機制進行了分析。在此基礎上提出了廣東醫療器械企業合作夥伴選擇的評價模型。通過質性分析（群組特徵根法）優化評價指標體系，然後通過量化分析（層次分析法）來確定評價體系中各層次指標的重要程度。本研究發現廣東醫療器械企業在選擇協同創新

合作夥伴時商業理念兼容、創新能力、關係互惠是最重要的考慮因素。企業專家們特別關注合作夥伴的戰略目標匹配、掌握的專利數量、行業影響、組織文化兼容、和研發緊密程度等指標。最後，本文進一步討論了結果中專家權重的理論與實踐意義。