The Experts below are selected from a list of 36 Experts worldwide ranked by ideXlab platform
Kira Erickson - One of the best experts on this subject based on the ideXlab platform.
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Opportunities and challenges for Minnesota Sewn Product manufacturers
Research Journal of Textile and Apparel, 2017Co-Authors: Elizabeth Bye, Kira EricksonAbstract:Purpose This study aims to explore domestic Sewn Product manufacturing in Minnesota. There is a renewed interest from consumers in Products Made in America, thus some manufactures are taking advantage of the opportunities to produce locally. Design/methodology/approach Fourteen companies from across the state that ranged in size and Product type were interviewed about the motivations, opportunities and challenges they experience with domestic manufacturing. A content analysis was conducted on the qualitative data. Findings The themes of personal values and economics emerged under motivations; the local economy, control and uniqueness were revealed as opportunities; and challenges included the themes of manufacturing resources and costs. Originality/value This group of Minnesota manufacturers holds strong values that drive them to balance the opportunities and challenges of domestic manufacturing. There is no evidence to determine if sustainability is a concern or a motivating factor.
Erickson Kira - One of the best experts on this subject based on the ideXlab platform.
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Minnesota Sewn Product Manufacturers: Opportunities and Challenges
Iowa State University Digital Repository, 2017Co-Authors: Bye Elizabeth, Erickson KiraAbstract:This research explored domestic Sewn Product manufacturing in Minnesota. There is growing interest in Products made domestically to benefit from the opportunities to produce locally. Fourteen companies from across the state were interviewed about their experience with domestic manufacturing. A content analysis was conducted. Looking at motivations, the themes of personal values and economics developed; the local economy, control, and uniqueness arose as opportunities; and challenges included manufacturing resources and costs. These Minnesota manufacturers are driven to balance the opportunities and challenges of domestic manufacturing in part due to their personal values. Sustainability was not identified as a concern or a motivating factor
Elizabeth Bye - One of the best experts on this subject based on the ideXlab platform.
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Opportunities and challenges for Minnesota Sewn Product manufacturers
Research Journal of Textile and Apparel, 2017Co-Authors: Elizabeth Bye, Kira EricksonAbstract:Purpose This study aims to explore domestic Sewn Product manufacturing in Minnesota. There is a renewed interest from consumers in Products Made in America, thus some manufactures are taking advantage of the opportunities to produce locally. Design/methodology/approach Fourteen companies from across the state that ranged in size and Product type were interviewed about the motivations, opportunities and challenges they experience with domestic manufacturing. A content analysis was conducted on the qualitative data. Findings The themes of personal values and economics emerged under motivations; the local economy, control and uniqueness were revealed as opportunities; and challenges included the themes of manufacturing resources and costs. Originality/value This group of Minnesota manufacturers holds strong values that drive them to balance the opportunities and challenges of domestic manufacturing. There is no evidence to determine if sustainability is a concern or a motivating factor.
Bye Elizabeth - One of the best experts on this subject based on the ideXlab platform.
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Minnesota Sewn Product Manufacturers: Opportunities and Challenges
Iowa State University Digital Repository, 2017Co-Authors: Bye Elizabeth, Erickson KiraAbstract:This research explored domestic Sewn Product manufacturing in Minnesota. There is growing interest in Products made domestically to benefit from the opportunities to produce locally. Fourteen companies from across the state were interviewed about their experience with domestic manufacturing. A content analysis was conducted. Looking at motivations, the themes of personal values and economics developed; the local economy, control, and uniqueness arose as opportunities; and challenges included manufacturing resources and costs. These Minnesota manufacturers are driven to balance the opportunities and challenges of domestic manufacturing in part due to their personal values. Sustainability was not identified as a concern or a motivating factor
James N. K. Liu - One of the best experts on this subject based on the ideXlab platform.
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WIPA: neural network and case base reasoning models for allocating work in progress
Journal of Intelligent Manufacturing, 2012Co-Authors: Lucas K. C. Lai, James N. K. LiuAbstract:Assembly Line Balance (ALB) problem is a typical combinatorial optimization problem where pieces of work are transported between the work stations. In the ALB problem, the ultimate goal is to seek the optimal makespan. It is a very difficult problem to solve particularly in Sewn Product Industry (SPI) which is a labor-intensive manufacturing industry. In order to achieve the optimal makespan, it is necessary to take into account factors such as the efficiency of each machinist, the allocation of suitable Work In Progress (WIP) into each assembly line, the calculation of each Product Production time in terms of Sewing Minute Value (SMV) and the assignment of each machinist into different work stations according to his/her capability. However, the current methodologies are dependent on human experts relying on statistical data. These data, however, are problematic in that they are historical data and as such are unlikely to be suitable for all circumstances especially as in a highly competitive industry such as the SPI practices, standards and tasks are constantly changing and adapting. In this paper, two models have been proposed to solve the WIP allocation problem and the SMV calculation problem. The preliminary results are encouraging. The first model is able to extract a large number of the rules and has attained a prediction accuracy of 93%. The second model can increase 11% in accuracy in predicting the SMV compared to the current widely used General Sewing Data (GSD) method.