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|Title:||A Model and Methodology for Quantitatively Measuring and Improving the Leanness of Discrete Manufacturing Applications|
|Abstract:||A focus of manufacturing management and engineering is towards increasing productivity through operational improvements with a goal to ensure profitability, and secure business viability/sustainability. With input cost increases and downward pressure on sales pricing and fluctuating volumes, there is a constant need to look at manufacturing efficiency improvements to control costs, improve delivery and ensure customer satisfaction. This thesis will provide a novel model formulation, giving a quantitative measurement to assess leanness of a real world-based, multiple-value-stream operation. This is done while providing a method to select a portfolio of real world-based initiatives to maximize the leanness of such an operation, given a variable profile of constraints in investment and resources. This model will yield clusters of projects based on the constraints available with the objective of having the leanest operation possible utilizing a Knapsack Problem approach. In addition, the model redraws leanness as a business-relevant metric covering cost, delivery, quality, utilization, level of lean implementation, leanness of layout and efficiency. A review of the current literature, relevant work as well as gaps in the current offerings is provided. A key element of this research is to have a novel model that can quantify leanness and use this information to improve performance by selecting a subset of lean improvement projects. Currently there is no industry standard known to the author that can be applied to estimate the leanness of an entire operation or any of its individual value streams or assess the subsequent leanness impact achieved by selecting an optimal subset of real-world improvement initiatives given practical constraints. The model proposed also incorporates the leanness of the facility layout and considers the level of lean implementation.|
|Appears in Collections:||Open Access Dissertations and Theses|
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|Arps_Arthur_W_2019Nov_PhD.pdf||2.87 MB||Adobe PDF||View/Open|
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