Supply chains are under pressure like never From climate-driven disruptions to geopolitical shifts, businesses must adapt to rising costs, new trade barriers and growing sustainability demands.<\/p>\n In this new world where supply chains face uncertainty,\u00a0Supply Chain Analytics is essential\u00a0<\/strong>to keep\u00a0resilient operations<\/strong>.<\/p>\n Samir, can you advise me on how to build a supply chain analytics portfolio with actual projects?<\/p>\n<\/blockquote>\n Since publishing my first post on Towards Data Science on August 5th, 2020, I\u2019ve frequently received this question from readers on LinkedIn<\/a> or YouTube<\/a>.<\/p>\n In this article, I\u2019ll share my perspective\u2014after nine years in the industry\u2014on how I would use my Supply Chain Analytics Cheat Sheet<\/a> to build a portfolio if I were starting out as a junior data scientist.<\/p>\n Let\u2019s start by defining the terms we use.<\/p>\n Supply Chain Analytics<\/a> refers to a set of tools and methodologies used to extract insights from data across all processes in the value chain.<\/p>\n For instance, a retail company may use:<\/p>\n As a Supply Chain Solution Manager and Data Scientist in the logistics industry, I have used analytics in international projects to design and optimize supply chain solutions.<\/p>\n I\u2019ve shared many of these methodologies and tools in over 75 articles published on Towards Data Science.<\/p>\n I\u2019ve compiled them into this concise and comprehensive Supply Chain Analytics Cheat Sheet<\/a>.<\/p>\n Whether you want to reduce distribution costs, minimize your company\u2019s environmental impact, or maximize profitability, you\u2019ll find the answers to your questions here.<\/p>\n How Can Analytics Help Improve Profitability?<\/p>\n<\/blockquote>\n The first section of the cheat sheet<\/a> is about data analytics for Business Strategy.<\/strong><\/p>\n It includes practical case studies on how to use data to support business executives in their strategic decision-making.<\/p>\n For instance, the series of articles, \u201cBusiness Planning with Python<\/a>\u201d, is based on a real example of a business managed by my friend.<\/p>\n \u201cWe have to refuse orders as we don\u2019t have enough cash to pay suppliers for stock replenishment.\u201d<\/p>\n<\/blockquote>\n I built a simulation model based on this insight to help him understand weaknesses in his value chain and uncover growth opportunities.<\/p>\n They illustrate how you can add value to small, medium, and large business owners.<\/p>\n What About Optimizing Supply Chain Operations?<\/p>\n<\/blockquote>\n Having spent years designing<\/strong>, monitoring<\/strong>, and optimizing <\/strong>supply chain solutions, I\u2019ve compiled many case studies focused on warehousing and transportation operations.<\/p>\n In this section, most case studies are based on an actual reengineering project I have conducted in Asia or Europe.<\/p>\n Country Manager: \u201cSamir, we need to reduce warehousing costs by 15% if we want to renew the contract with the retail company\u00a0XXX.\u201d<\/p>\n<\/blockquote>\n They focus on optimizing a specific process in a warehouse (order preparation, value-added services<\/a>) or transportation operations<\/a> (routing, scheduling).<\/p>\n Go to the nearest warehouse and ask: \u2018What are your problems?\u2019 You can be sure they will find some for\u00a0you.<\/p>\n<\/blockquote>\n Here\u2019s how to get started:<\/p>\n The code is usually a simple Python<\/a> script or a jupyter notebook that can be easily adapted.<\/p>\n What if you want to have a greater impact? Focus on a flow optimization.<\/p>\n<\/blockquote>\n The main driver of the reengineering projects I have conducted was cost.<\/p>\n Usually, customers tracked logistics costs, i.e. the percentage of turnover spent on logistic operations.<\/p>\n Therefore, we needed to find solutions (as a third-party logistic service provider) to reduce this percentage without impacting our profitability.<\/p>\n What if we delivered to the U.S. East Coast from a warehouse in Charlotte?<\/p>\n<\/blockquote>\n The solutions presented in the previous section are too localized. We need to take a step back and consider flow optimization.<\/p>\n These case studies focus on the optimization of goods flow using<\/p>\n For some case studies, I have deployed the models in a web application developed for my startup, LogiGreen<\/a>.<\/p>\n The demo version is publicly available for you to test the models; more information here.<\/a><\/p>\n What about sustainability?<\/p>\n<\/blockquote>\n If you want to support the green transformation of your company, I have some examples for you.<\/p>\n Since my first project focused on sustainability, I was convinced that green transformation was similar to supply chain optimization.<\/p>\n Therefore, you can find 17 examples of optimization solutions <\/strong>using this approach to minimize CO2 emissions or resource usage.<\/p>\n I also decided to cover the reporting side of sustainability with analytics for Life Cycle Assessment<\/strong>, CO2 emissions calculations<\/strong> or ESG reporting<\/strong>.<\/p>\n If you need support getting started with sustainability projects, you\u2019re covered.<\/p>\n Now It\u2019s Your Turn to Build!<\/p>\n<\/blockquote>\n If you need a more detailed presentation of the cheat sheet, check out this short YouTube tutorial.<\/p>\n\n
The Supply Chain Analytics Cheat Sheet<\/h2>\n
What is Supply Chain Analytics?<\/h3>\n
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<\/a>What\u2019s Inside the Supply Chain Analytics Cheat Sheet?<\/h3>\n
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Data Analytics to Boost Business Profitability<\/strong><\/h6>\n
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Supply Chain Analytics for Logistics Operations<\/h6>\n
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Data Analytics for Supply Chain Optimization<\/h4>\n
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Supply Chain Analytics for Sustainability<\/h4>\n
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