Supply chain segmentation Analytics with Python

所在平台: Udemy

课程主页: https://www.udemy.com/course/supply-chain-segmentatiob-analytics-with-python/

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课程简介

课程名称:使用Python进行供应链细分分析 课程概述:供应链问题因SKU、客户和供应商数量庞大而复杂。任何供应链分析项目的第一步是减少问题的复杂性,而供应链细分是实现这一目标的基础步骤。通过实施供应链细分,我们可以更轻松地选择合适的需求预测模型、优化库存或根据不同细分采取相应策略。本课程将教授最常用的ABC和XYZ细分分析,并结合两者的应用。学习本课程的关键优势是,课程结束时,您将掌握一套完整的代码,以便对实际案例中多个门店和超过3000个SKU进行ABC和XYZ分析。此外,在构建细分算法的同时,我们还将实现Python中的互动可视化,使您能够以用户友好的方式向经理或客户展示和解释分析结果。本课程将作为您供应链分析项目的基础。如果您是供应链专业人士,学习本课程后,可以利用公司数据实施ABC和XYZ细分分析,为组织提供洞察。如果您是供应链学生,这些知识将帮助您深入理解更复杂的话题,并可为您的供应链分析项目构建作品集。

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课程详情

Supply chain problems are complex due to large number of SKU's, Customers and Suppliers, as a first step in any supply chain analytics project we need to reduce the complexity of the problem. Supply chain segmentation is a fundamental first step that enables us to reduce the supply chain complexity. Once we perform the supply chain segmentation, we can easily start choosing the proper demand forecasting model, can optimize the inventory, or can implement any kind of strategy based on the segments.In this course we learn the most commonly used ABC and XYZ segmentation analysis, as well as we combine the both. The key advantage of learning this course is at the end of the lesson you will have a complete code to perform ABC, XYZ analysis on real life use cases with multiple stores and more than 3000 skus. In addition to building the segmentation algorithms we will implement interactive visualizations in python, so you can present and explain your analysis in a user friendly manner to your manager or to your client.This course will serve as a first building block for your supply chain analytics project. If you are a supply chain professional, after learning in this course you can use your company data to implement the ABC, XYZ segmentation analysis to derive insights for your organization. If you are a supply chain student you can use this knowledge to understand much deeper topics as well as can build your portfolio of supply chain analytics projects.

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