Some researchers have found that unionized firms are less likely to pursue automation because high wage demands deprive them of the necessary capital required to invest in advanced manufacturing technology (AMT). It has also been suggested that stringent work rules and technology agreements can make the substitution of new technology for union labor too expensive. Others have found, however, that the pursuit of high wage policies and the resultant requirement for improved worker and machine productivity can create a positive environment for technological change. This exploratory study examines the relationships between firm-level union status and the adoption and performance of AMT in the discrete parts durable-goods manufacturing industry. Analyses of our sample, which included Chi-square tests, t-tests, correlation analyses and multiple linear regression analyses, revealed a union effect on the adoption of just-in-time technology and a moderately positive union effect on performance. Results of analyses of the impact of union status, firm size and several human factor variables on firm performance are also presented and discussed.

In an increasingly technology-based competitive global business environment, the operational and competitive strategic potentials of advanced manufacturing technologies (AMT) and related systems cannot be overlooked. This article presents the results of an investigation of AMT implementation practices at 82 discrete-parts durable goods manufacturing plants in the USA. Several propositions that were derived from the AMT literature are tested. The results of this investigation indicate that plants that are desirous of adopting integrated technologies should be prepared to exert considerable effort on the activities in the pre-planning and justification stages of the implementation process. These and other findings that will be particularly useful to firms in the pre-planning stages of technology adoption are outlined and discussed. Research implications of this study are also presented and discussed.

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Manufatura aditiva é um processo de fabricação de objetos que tem chamado a atenção de acadêmicos, empresas e órgãos de governo. Alguns setores específicos possuem demandas que podem ser mais bem atendidas com a utilização da manufatura aditiva na produção de peças de alto valor agregado. O problema prático que motiva a realização deste trabalho é a dificuldade enfrentada pelas empresas em avaliar as tecnologias de manufatura aditiva existentes para decisão de adoção ou não adoção delas em seus fluxos produtivos. A análise da situação atual na literatura indica que falta um modelo de processo específico de avaliação para adoção da manufatura aditiva na indústria de alto valor agregado. Visando preencher essa lacuna e propor uma solução para o problema identificado, este trabalho tem como objetivo a definição de um modelo de processo específico de avaliação para adoção de manufatura aditiva na indústria de alto valor agregado. Tal modelo é desenvolvido a partir de modelos de processo existentes para avaliar a adoção de outras tecnologias de manufatura avançada (Advanced Manufaturing Technologies - AMTs) e do levantamento das características específicas da manufatura aditiva. O modelo de processo de avaliação para adoção da manufatura aditiva na indústria de alto valor agregado proposto contempla nove passos para decidir sobre a adoção ou a não adoção da manufatura aditiva. A aplicabilidade do modelo proposto foi avaliada por meio de entrevistas com especialistas. A avaliação dos resultados indica que os passos e as atividades propostos no modelo contribuem para auxiliar na avaliação para adoção da manufatura aditiva na indústria de alto valor agregado. / Additive manufacturing is a manufacturing process that has gained attention of scholars, companies and government bodies. Some specific sectors have demands that can be better met with the use of additive manufacturing to produce high added value parts. The practical problem that motivates this work is the difficulty met by companies in assessing additive manufacturing technologies for decision to adopt or not adopt them in their production flows. The analysis of the current situation in the literature indicates a gap of a specific process model to evaluate the adoption of additive manufacturing in the high added value industry. To fill this gap and to propose a solution for the identified problem, this work has the goal to define a specific process model to evaluate the adoption of additive manufacturing in the high added value industry. This model is developed based on existing process models to evaluate the adoption of other Advanced Manufacturing Technologies and the study of specific characteristics of additive manufacturing. The model proposed contemplates nine steps to decide on the adoption or not adoption of additive manufacturing. The applicability of the proposed model was evaluated through interviews with experts. The evaluation of the results indicates that the steps and the activities proposed in the model contribute to assist in the evaluation of the adoption of additive manufacturing in high added value industry.

Hvordan kan en omkostningskalkulationsmodel udformes under moderne og fleksible produktionsforudsætninger, og hvordan påvirker stokastikken fra produktionen en given kalkulationsmodel, når der tages højde for samtlige indsatte ressourcer fra produktionen? Disse forhold er diskuteret med udgangspunkt i den existerende kalkulationsteori på området og i relation till to konkrete case-virksomheder. For at kunne gøre konkrete beregninger af stokastikkens virkninger, er der udformet en model baseret på et FMS-system, som har været testet via stokastisk simulering. Resultatet heraf viste, at variationer i processerne, transport og leadtid kan have relativ stor effekt på stykomkostningerne sammenliget med det deterministiske tilfælde. Med en stokastisk omkostningsmodel er der også mulighed for, at estimere effekten fra Design For Manufacturability (DFM) via standardafvigelsen. Dermed bliver det muligt att søge efter at minimere stokastikken og variationen fra produktionen. / Diss. Stockholm : Handelshögskolan

British manufacturing Abstraot Companies have been slower to automate their facilities, and computerise their information systems, than many of their overseas competitors in Europe, North America and Japan. Initially, this research studied advanced manufacturing technology, (AMT), systems theory, the UK economy and investigated the underlying reasons for and against company' s decisions to automate. Automating procedures were studied for a sample of 20 Engineering companies with particular attention paid to their; systemic approach to implementing AMT, inter-business activity communications, individual company strategies, operational tactics, and implications from previous installations. This information was supported by questionnaires targeted at UK design engineers' and equipment suppliers. Interviews with Trade Unions, financial institutions, professional institutions and Government, were also arranged. The research found that correctly implemented AMT, with the optimum balance of flexibility and complexity, improved businesses' competitiveness, although many operational efficiencies could be attained merely by rationalising existing systems. When a company implements AMT it is critical that they synchronise the equipment with additional complementary systems and manufacturing resources. However, every company has their own unique solutions due to the historical evolution of factory facilities, product ranges and employee skills. The restrictive practices adopted the financial accountants and many of the Trade Union were found to restrain the rate of implementation for AMT and the move towards total integrated businesses. The research analysis yielded a ten point model for the strategic and tactical management of advanced manufacturing systems. Finally, the work concludes by identifying "accounting systems", and procedures for "designing for manufacture", as areas which deserve further investigation.

Manufatura aditiva é um processo de fabricação de objetos que tem chamado a atenção de acadêmicos, empresas e órgãos de governo. Alguns setores específicos possuem demandas que podem ser mais bem atendidas com a utilização da manufatura aditiva na produção de peças de alto valor agregado. O problema prático que motiva a realização deste trabalho é a dificuldade enfrentada pelas empresas em avaliar as tecnologias de manufatura aditiva existentes para decisão de adoção ou não adoção delas em seus fluxos produtivos. A análise da situação atual na literatura indica que falta um modelo de processo específico de avaliação para adoção da manufatura aditiva na indústria de alto valor agregado. Visando preencher essa lacuna e propor uma solução para o problema identificado, este trabalho tem como objetivo a definição de um modelo de processo específico de avaliação para adoção de manufatura aditiva na indústria de alto valor agregado. Tal modelo é desenvolvido a partir de modelos de processo existentes para avaliar a adoção de outras tecnologias de manufatura avançada (Advanced Manufaturing Technologies - AMTs) e do levantamento das características específicas da manufatura aditiva. O modelo de processo de avaliação para adoção da manufatura aditiva na indústria de alto valor agregado proposto contempla nove passos para decidir sobre a adoção ou a não adoção da manufatura aditiva. A aplicabilidade do modelo proposto foi avaliada por meio de entrevistas com especialistas. A avaliação dos resultados indica que os passos e as atividades propostos no modelo contribuem para auxiliar na avaliação para adoção da manufatura aditiva na indústria de alto valor agregado. / Additive manufacturing is a manufacturing process that has gained attention of scholars, companies and government bodies. Some specific sectors have demands that can be better met with the use of additive manufacturing to produce high added value parts. The practical problem that motivates this work is the difficulty met by companies in assessing additive manufacturing technologies for decision to adopt or not adopt them in their production flows. The analysis of the current situation in the literature indicates a gap of a specific process model to evaluate the adoption of additive manufacturing in the high added value industry. To fill this gap and to propose a solution for the identified problem, this work has the goal to define a specific process model to evaluate the adoption of additive manufacturing in the high added value industry. This model is developed based on existing process models to evaluate the adoption of other Advanced Manufacturing Technologies and the study of specific characteristics of additive manufacturing. The model proposed contemplates nine steps to decide on the adoption or not adoption of additive manufacturing. The applicability of the proposed model was evaluated through interviews with experts. The evaluation of the results indicates that the steps and the activities proposed in the model contribute to assist in the evaluation of the adoption of additive manufacturing in high added value industry.

No description available.

Modern manufacturing needs to optimize the entire product lifecycle to satisfy the customer needs. The advancement of sensing technologies has brought a data rich environment for manufacturing and provide a great opportunity for real-time, proactive quality assurance. However, due to the lack of methods for analyzing heterogeneous types of data, the transformation of data to information and knowledge for effective decision making in manufacturing is still a challenging problem. In particular, functional variables can represent the in situ process conditions and rich product performance information, and are widely encountered in various manufacturing processes. In this dissertation, I will focus on modeling of manufacturing processes with in situ process (functional) variables, and integrating these functional variables and other measured variables for the manufacturing modeling. The modeling is explored by extracting informative features through the integration of multiple functional variables, functional variables and offline setting variables, and quantitative and qualitative quality variables. After an introduction in Chapter 1, three research tasks are investigated. First, a functional variable selection problem is studied in Chapter 2 to identify the significant functional variables as well as their features in a logistic regression model. A hierarchical non-negative garrote constrained estimation method is proposed. Second, the quality-process relationships for scalar offline setting variables, functional in situ process variables, and manufacturing quality responses are studied in Chapter 3. A functional graphical model that can integrate functional variables in a graphical model is proposed and investigated. Third, the quantitative and qualitative quality responses are jointly modeled with scalar offline setting variables and functional in situ process variables in Chapter 4. A functional quantitative and qualitative model is proposed and investigated. Finally, I summarize the research contribution and discuss future research directions in Chapter 5. The proposed methodologies have broad applications in manufacturing processes with functional variables, and are demonstrated in a crystal growth process with multiple functional variables (Chapter 2), a plasma spray process with multiple scalar and functional variables (Chapter 3), and an additive manufacturing process called fused deposition modeling with quantitative and qualitative quality responses (Chapter 4). / Ph. D.

The growing adoption of smart manufacturing systems and its related technologies (e.g., embedded sensing, internet-of-things, cyber-physical systems, big data analytics, and cloud computing) is promising a paradigm shift in the manufacturing industry. Such systems enable extracting and exchanging actionable knowledge across the different entities of the manufacturing cyber-physical system and beyond. From a quality control perspective, this allows for more opportunities to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. However, a multitude of challenges are arising, with the growing adoption of smart manufacturing, including industrial data characterized by increasing volume, velocity, variety, and veracity, as well as the security of the manufacturing system in the presence of growing connectivity. Taking advantage of these emerging opportunities and tackling the upcoming challenges require creating novel quality control and data analytics methods, which not only push the boundaries of the current state-of-the-art research, but discover new ways to analyze the data and utilize it. One of the key pillars of smart manufacturing systems is real-time automated process monitoring, diagnosis, and control methods for process/product anomalies. For machining applications, traditionally, deterioration in quality measures may occur due to a variety of assignable causes of variation such as poor cutting tool replacement decisions and inappropriate choice cutting parameters. Additionally, due to increased connectivity in modern manufacturing systems, process/product anomalies intentionally induced through malicious cyber-attacks -- aiming at degrading the process performance and/or the part quality -- is becoming a growing concern in the manufacturing industry. Current methods for detecting and diagnosing traditional causes of anomalies are primarily lab-based and require experts to perform initial set-ups and continual fine-tuning, reducing the applicability in industrial shop-floor applications. As for efforts accounting for process/product anomalies due cyber-attacks, these efforts are in early stages. Therefore, more foundational research is needed to develop a clear understanding of this new type of cyber-attacks and their effects on machining processes, to ensure smart manufacturing security both on the cyber and the physical levels. With primary focus on machining processes, the overarching goal of this dissertation work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on adopting this goal in three distinct areas of interest: (1) Statistical Process Monitoring of Time-Between-Events Data (e.g., failure-time data); (2) Defending against Product-Oriented Cyber-Physical Attacks on Intelligent Machining Systems; and (3) Modeling Machining Process Data: Time Series vs. Spatial Point Cloud Data Structures. / PHD / Recent advancements in embedded sensing, internet-of-things, big data analytics, cloud computing, and communication technologies and methodologies are shifting the modern manufacturing industry toward a novel operational paradigm. Several terms have been coined to refer to this new paradigm such as cybermanufacturing, industry 4.0, industrial internet of things, industrial internet, or more generically smart manufacturing (term to be used henceforth). The overarching goal of smart manufacturing is to transform modern manufacturing systems to knowledge-enabled Cyber-Physical Systems (CPS), in which humans, machines, equipment, and products communicate and cooperate together in real-time, to make decentralized decisions resulting in profound improvements in the entire manufacturing ecosystem. From a quality control perspective, this allows for more opportunities to utilize manufacturing process data to realize proactive product design; real-time process monitoring, diagnosis, prognosis, and control; and better product quality characterization. With primary focus on machining processes, the overarching goal of this work is to explore new ways to expand the use and value of manufacturing data-driven methods for better applicability in industrial shop-floors and increased security of smart manufacturing systems. As a first step toward achieving this goal, the work in this dissertation focuses on three distinct areas of interest: (1) Monitoring of time-between-events data of mechanical components replacements (e.g., failure-time data); (2) Defending against cyber-physical attacks on intelligent machining systems aiming at degrading machined parts quality; and (3) Modeling machining process data using two distinct data structures, namely, time series and spatial point cloud data.