This master’s thesis investigates a new approach of pull systems sizing in lean manufacturing, focusing on two key applications: purchasing and production. The traditional formula for pull system dimensioning, while widely used, often lacks precision due to the many factors influencing stock levels and consumption trends. Through an internship in auxiell Spa, a consultancy firm specializing in lean transformations, this project aimed to address these challenges by developing a more accurate method. The new formula was designed to adapt to real-world variables affecting both purchasing and manufacturing environments. After initial testing in Excel to simulate stock trends, the analysis was extended and refined in Python, allowing for a more comprehensive evaluation across larger datasets. The comparative analysis between the traditional and new formulas demonstrated significant improvements in reducing average stock levels, lowering inventory costs, and enhancing system efficiency. Moreover, it provides valuable insights by offering a flexible framework that can be applied to various real-world scenarios. The new formula allows users to explore key factors such as order frequency, supplier lead times, and service levels. By adjusting variables, the formula can be customized to suit different production or purchasing environments, making it a versatile tool for improving inventory management. This analysis aims to minimize stock levels while maximizing service levels, as well as to identify common patterns that could be useful for improving value delivery in other clients or situations. Additionally, the model helps highlight both qualitative and quantitative benefits compared to the traditional formula.
This master’s thesis investigates a new approach of pull systems sizing in lean manufacturing, focusing on two key applications: purchasing and production. The traditional formula for pull system dimensioning, while widely used, often lacks precision due to the many factors influencing stock levels and consumption trends. Through an internship in auxiell Spa, a consultancy firm specializing in lean transformations, this project aimed to address these challenges by developing a more accurate method. The new formula was designed to adapt to real-world variables affecting both purchasing and manufacturing environments. After initial testing in Excel to simulate stock trends, the analysis was extended and refined in Python, allowing for a more comprehensive evaluation across larger datasets. The comparative analysis between the traditional and new formulas demonstrated significant improvements in reducing average stock levels, lowering inventory costs, and enhancing system efficiency. Moreover, it provides valuable insights by offering a flexible framework that can be applied to various real-world scenarios. The new formula allows users to explore key factors such as order frequency, supplier lead times, and service levels. By adjusting variables, the formula can be customized to suit different production or purchasing environments, making it a versatile tool for improving inventory management. This analysis aims to minimize stock levels while maximizing service levels, as well as to identify common patterns that could be useful for improving value delivery in other clients or situations. Additionally, the model helps highlight both qualitative and quantitative benefits compared to the traditional formula.
Pull System in Lean Production: a critical analysis of parameters influencing kanban sizing
DOTTO, ANDREA
2023/2024
Abstract
This master’s thesis investigates a new approach of pull systems sizing in lean manufacturing, focusing on two key applications: purchasing and production. The traditional formula for pull system dimensioning, while widely used, often lacks precision due to the many factors influencing stock levels and consumption trends. Through an internship in auxiell Spa, a consultancy firm specializing in lean transformations, this project aimed to address these challenges by developing a more accurate method. The new formula was designed to adapt to real-world variables affecting both purchasing and manufacturing environments. After initial testing in Excel to simulate stock trends, the analysis was extended and refined in Python, allowing for a more comprehensive evaluation across larger datasets. The comparative analysis between the traditional and new formulas demonstrated significant improvements in reducing average stock levels, lowering inventory costs, and enhancing system efficiency. Moreover, it provides valuable insights by offering a flexible framework that can be applied to various real-world scenarios. The new formula allows users to explore key factors such as order frequency, supplier lead times, and service levels. By adjusting variables, the formula can be customized to suit different production or purchasing environments, making it a versatile tool for improving inventory management. This analysis aims to minimize stock levels while maximizing service levels, as well as to identify common patterns that could be useful for improving value delivery in other clients or situations. Additionally, the model helps highlight both qualitative and quantitative benefits compared to the traditional formula.File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/74658