17 research outputs found

    Acceleration management: the semiconductor industry confronts the 21st century

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    In the recent generations of semiconductor devices, the semiconductor industry has been accelerating towards the limits of the physical sciences. As a consequence, technology managers in that industry face seven major challenges, which will threaten progress: process, complexity, performance, power, density, productivity, and quality / reliability. We believe that confronting these challenges requires a new approach to technology management both within organizations and between organizations that form the backbone of the industry. We call this new approach Acceleration Management. Acceleration Management first requires that firms cultivate deep technical knowledge and inspire creative solutions to seemingly insoluble technical problems. The second stage of Acceleration Management requires the necessary expertise to be pooled, which often demands inter-organizational cooperation. This paper explores these managerial imperatives and analyzes how new semiconductor firms--particularly in China--have created niches in the value chain even during a tumultuous time in the industry\u27s history

    Production Research in China

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    (First paragraph) China is the world’s largest emerging economy. In recent years, China has moved to an increasingly market-oriented economy that opens to international trade and investment. At the same time, the popularity of China as a manufacturing base, assembling goods for sale worldwide is growing. In addition to global manufacturers who have built their own plants in China, many manufacturing companies are outsourcing production to Chinese subcontractors and branding the products with their own logos. In today’s China, production research is becoming more and more important; advanced production research becomes an important enabler to make its manufacturing industry competitive. Although China has different financial, legal, and physical infrastructure, production researchers in China have successfully proven that production research can help to manage global manufacturing competition

    Optimizing inventory levels using financial, lifecycle and forecast variance data

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    Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science; in conjunction with the Leaders for Manufacturing Program at MIT, 2007.Includes bibliographical references (leaves 47-48).Significant inventory write-offs have recently plagued ATI Technologies, a world leader in graphics and media processors. ATI's product-centric culture has long deterred attention from supply chain efficiency. Given that manufacturing lead time exceeds customer order lead time for its semiconductors, ATI relies heavily on their demand forecasting team to instigate supply chain activities. The PC business unit forecasting team translates market information into product-line forecast and also sets finished goods inventory levels intended to offset demand uncertainty. Today's inventory decisions are made in response to customer escalations, often ignoring financial implications. To add necessary rigor when setting these inventory levels, this thesis presents a model using wafer and unit cost, profit margin, product lifecycle stage and historical forecast error to categorize products into inventory risk levels. The resultant risk levels become a critical input to monthly demand-supply meetings with marketing, operations and senior executives - the outcome of which are wafer orders and assembly and test plans at the world's largest contract foundries and subcontractors. Finally, the 2006 acquisition of ATI by Advanced Micro Devices (AMD) offers unforeseen flexibility, scale and challenges to the outsourced semiconductor supply chain.by Irene S. Hwang.S.M.M.B.A

    A decision model for manufacturing best practice adoption : linking practices to competitive strategies

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    This thesis describes research that has developed a decision model for the analytical selection of manufacturing best practices. The competitiveness and growth in the manufacturing sector is critical for Singapore economy. Design and improvement of manufacturing systems is imperative to sustain the competitiveness of manufacturing organisations in the country. It is common for companies to adopt manufacturing best practices in this design process to emulate the success and performance of their counterparts. However, practices should be adapted to the competitive environment and strategy of the company to yield the desired results. Therefore, linkages between best practices and their associated competitive priorities will present useful guidelines for action to help manufacturing organisations achieve superior performance. The research programme has set out to define a decision model for best practice adoption. A broad taxonomy of manufacturing strategies and concepts has been used to identify and cluster a list of popular best practices commonly adopted. The decision framework for best practice adoption process is then formulated and a preliminary decision model constructed. This model is verified through semistructured interviews with industry and academic experts. Validation of model is conducted via case study research on eight manufacturing organisations. Linkages between practices and competitive strategies are then constructed to establish the final decision model. Finally, this decision model is illustrated in the form of a guidebook to help practitioner in the best practice selection process. This research has bridged the fields of manufacturing strategy and best practice research by establishing a comprehensive taxonomy of manufacturing strategies and concepts to classify the popular and commonly adopted best practices. A decision model that links best practices to competitive strategies has been developed to select the most appropriate practices for an environment. Thus, the work presented in this thesis has made a significant and original contribution to knowledge on the provision of analytical decision support for practitioners engaging in the manufacturing best practice adoption process.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

    Web site design, e-collaboration and e-business for a low-volume and high-mix business

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    Thesis (M.B.A.)--Massachusetts Institute of Technology, Sloan School of Management; and, (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; in conjunction with the Leaders for Manufacturing Program at MIT, 2001.Includes bibliographical references (leaf 84).by Gregory Mont Thornton.S.M.M.B.A

    Distributed Infrastructuring and Innovation: an ethnographic enquiry into collaborative modes of work in an internet of things ecosystem

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    Emerging low-power wireless networks are being used for a range of data collection systems such as asset tracking, environmental monitoring, smart agriculture and smart city facilities. The relatively low costs of hardware components, modular network architectures and open standards are allowing a diversity of new actors to engage with the construction of ‘internet of things’ (IoT) networks and applications. Various branches of research within management studies, critical theory, design theory, feminism and science and technology studies (STS) have explored collaborative modes of technology development among heterogeneous groups of actors and addressed questions of how and why users become involved in technology development. There is however scant empirical and theoretical work on the involvement of ‘users’ and other non-conventional actors in contemporary data-oriented infrastructures such as the IoT. Conjointly, most policy roadmaps concerning the rise of pervasive data networks rely primarily on industry-oriented analyses and quantitative forecasts and hence remain blind to the involvement of non-corporate actors in the shaping of technological futures. Building on an STS-inflected framework, this study contributes to bridging this gap with a micro-level enquiry into collaborative work practices in the realm of the IoT. This thesis explores the case of The Things Network, an initiative with the mission to build low-power wireless networks in a decentralised fashion with a strong reliance on geographically dispersed contributors. The initiative is far removed from traditional top-down infrastructure implementation strategies and faces a range of ambivalences related to organisation, growth and sustainability. The study is concerned with the questions of what types of work, social organisations and artefacts are subsumed in the emerging ecosystem? why/how contributors organise and operate local networks? whether and how control is exerted by the project owners? and how the uneven actions of users and other non-conventional actors are implicated in the generation of technical improvements and outcomes? The methodology comprised a multi-site ethnographic exploration over two and a half years with the practitioners contributing variously to the construction of data networks and the development of IoT solutions within the initiative. An ecological analysis is developed, drawing on theories and concepts from infrastructure studies and the social shaping of technology framework. The evolution of the initiative is traced throughout the stages of inception, early scaling up and global expansion. Through casting low-power networks as ‘data infrastructure’, the analysis foregrounds the challenges and dilemmas associated with scaling up in the context of decentralisation. The concept of ‘distributed infrastructuring’ is proposed as a means to capture the orchestration of the piecemeal work of disparate and dispersed actors operating autonomously with a common network architecture. The findings suggest that this mode of infrastructuring is symptomatic of an industry trend towards an increasing fragmentation and distribution of professional development activities among a range of actors. We conclude that policy and practice would benefit from a nuanced recognition of the diversity of contributions, positionalities and preferences in the broad landscape of data-driven technologies

    Globalization of R&D: Leveraging Offshoring for Innovative Capability and Organizational Flexibility

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    Within the realm of globalization of R&D, offshoring is a relatively recent and still emerging phenomenon. Rooted in the notion of comparative advantage, offshoring of R&D involves disaggregation and global distribution of the firm’s R&D value chain activities to leverage innovation capacity of low-cost countries. Characteristically different from market- and technology-seeking globalization of R&D, offshoring is motivated by the intertwining competitive needs to gain efficiency and access knowledge resources. This study represents a systematic, grounds-up attempt to explore the terrain of the phenomenon of offshoring of R&D and its influence on the competitive advantage of firms. Specifically, going beyond structural cost savings, the research examines the link between offshoring of R&D and the firm’s innovative capability and organizational flexibility—the two most important organizational capabilities of high technology firms. Employing an interpretive approach, the research includes multiple case studies of intra-firm and inter-firm offshoring of software R&D across a range of industries. The study demonstrates that by strategically organizing and managing offshoring of R&D, firms can significantly enhance their innovative capability and organizational flexibility. The findings suggest that offshoring of R&D is a new global organizational form that not only serves as an adaptive device but also allows firms to achieve ambidexterity
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