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Thesis Draft-Final Version Looking Ahead By Looking Back “Understanding evolution of innovation in manufacturing” A thesis SUBMITTED TO THE FACULTY OF THE UNIVERSITY OF MINNESOTA BY Hammad Naeem IN PARTIAL FULLFILMENT OF REQUIREMENTS FOR DEGREE OF MASTER OF SCIENCE IN ENGINEERING MANAGEMENT Supervisors Dr. Emmanuel Enemuoh J. Moe Benda September, 2018 © Hammad Naeem 2018 ALL RIGHTS RESERVED ii Acknowledgement I want to thank all of my teachers, colleagues, friends and fellow classmates for their support and fruitful advice in completion of this thesis. I want to especially thank my supervisor, Dr. Emmanuel Enemuoh for his consistent guidance, valuable advice, patience, strong believe in me and most importantly providing me due freedom to explore my research potential during the completion of this thesis. His calm nature amalgamated with years of experience offered me to learn a lot academically as well as professionally. I also want to thank Mr. Moe Benda for in depth discussions about the idea of this thesis and providing all the insight from his professional experience. His idea “look ahead by looking back” remained the driving force of this thesis. I also like to thank my wife Hareema, whose resolute support and patience, providing me all the time I need to complete this thesis, which eventually helped me concentrate on my thesis. I also like to thank my parents whose prayers and encouraging words helped me in the completion of my project. i Abstract To understand the evolution of manufacturing and its future requires multidimensional study of different historical milestones, systems developed over a period of time and some concrete analysis amalgamated with experimental results. This thesis is about identifying major milestones in the manufacturing history and then using this information to understand the evolution of innovation process and innovative models. Further, using knowledge obtained from the study of innovation processes to understand the modern trends in manufacturing industry. Experimental analysis is performed using modern machine learning techniques like deep learning to correctly identify human facial expressions. The increasing utility of artificial intelligence was the driving force to exploit modern machine learning techniques that have proven that now decision power can be carefully delegated or shared with these intelligent systems. The Deep Learning based approach using convolutional neural network is tested on human facial expression recognition and accuracy of over 86% is achieved which is higher than other mathematical based machine learning models. These modern machine learning algorithms are also tested on numerical dataset to prove their flexibility and adaptability for different applications which can be faced in any modern day manufacturing industry. The results from this study show that these modern machine learning algorithms have outperformed old decision making methodologies due to their capacity and intelligence in learning different patterns present in the data and correspondingly helping in correct decision making. As a future recommendation, a hybrid system is proposed which is a combination of predictive as well as corrective maintenance. The proposed system is based on deep learning using convolutional neural network to predict end of life of a part. ii Table of Contents: List of Figures ……………………………………………………………….………….. v List of Tables …..……………………………………………………………………...... vi Chapter 1: Craftsmanship to High-tech Production .................................................................. 1 1.1 Importance of Manufacturing .................................................................................................... 2 1.2 Composition of Thesis ............................................................................................................... 4 Chapter 2: History of Manufacturing .......................................................................................... 5 2.1 History of Manufacturing through Centuries: ............................................................................ 5 2.1.1 Sixteenth Century .............................................................................................................. 11 2.1.2 Seventeenth Century ......................................................................................................... 14 2.1.3 Eighteenth Century ........................................................................................................... 19 2.1.4 Nineteenth Century ........................................................................................................... 27 2.1.5 Twentieth Century ............................................................................................................ 33 2.1.6 Twenty First Century ........................................................................................................ 46 Chapter 3: Innovation Management .......................................................................................... 50 3.1 Innovation Management Models ......................................................................................... 54 3.1.1 Technology Push Model ............................................................................................... 54 3.1.2 Market Pull Model ........................................................................................................ 55 3.1.3 Coupling Model ............................................................................................................ 55 3.1.4 Integrated Model ........................................................................................................... 55 3.1.5 Functional Integration Innovation model ...................................................................... 55 3.2 Innovation Process Models .................................................................................................. 56 3.3 Innovation Processes and Routines ...................................................................................... 57 3.3.1 Idea Generation and Searching ..................................................................................... 58 3.3.2 Idea Selection ................................................................................................................ 58 3.4 Factors Effecting Innovation Management .......................................................................... 61 3.4.1 Environment and Organizational Factors ..................................................................... 61 3.4.2 Country Environment .................................................................................................... 61 3.4.3 External Factors ............................................................................................................ 61 3.4.4 Organizational Demography ......................................................................................... 62 iii 3.4.5 Organizational Structure ............................................................................................... 62 3.4.6 Facilitate Resources ...................................................................................................... 63 3.4.7 Team Composition ........................................................................................................ 63 3.4.8 Communication Patterns ............................................................................................... 64 3.4.9 Finding Alternative solutions ........................................................................................ 64 3.5 Characteristics of CEO or Top Management ....................................................................... 64 3.5.1 Personal Traits of CEO or Top Management ................................................................ 65 3.6 Future of Manufacturing: Computer based Innovative Manufacturing ............................... 66 3.6.1 Future of Innovation: Role of Computers ..................................................................... 67 Chapter 4: Deep Learning Based Learning ............................................................................... 70 4.1 Overview of Proposed Approach ......................................................................................... 73 4.2 Model Architecture .............................................................................................................. 74 4.3 Deep Expression Convolutional Networks .......................................................................... 76 4.4 Supervised Regression ............................................................................................................. 80 Chapter 5: Conclusion ................................................................................................................. 84 Chapter 6: Future Recommendations ........................................................................................ 87 6.1 A Deep Learning Approach for Predictive Decision Making .................................................. 87 6.1.1 Deep Learning ................................................................................................................... 87 6.1.2 Technology Forecasting .................................................................................................... 88 6.1.3 Deep Learning Based Technology Forecasting ................................................................ 89 References ....................................................................................................................................
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