Find your Dream Job in Supply Chain Analytics — Create your Portfolio!
A practical guide for aspiring data scientists to create impactful, real-world supply chain analytics projects using case studies shared in this blog.
In 2025, global supply chains face unprecedented challenges.
From climate-related disruptions to geopolitical uncertainties, companies face escalating costs, emerging trade regulations and intensifying sustainability requirements.
In this context, mastering Supply Chain Analytics becomes a strategic competence for companies needing resilience.
You can bet on this need for your next career move.
Helllo Samir! How can I build a strong portfolio in supply chain analytics with practical projects?
This is the question readers frequently ask me via LinkedIn and YouTube since I published my first case study on Medium back on August 5th, 2020.
In this article, I will put myself in the shoes of a junior data scientist who wants to build their portfolio using the Supply Chain Analytics Cheat Sheet.
We will answer her questions together
- What is Supply Chain Analytics?
- How do you select a project to start your portfolio?
- What can you find in the Supply Chain Analytics cheat sheet?
- What skills do you need to showcase to convince companies?
Let’s go!
Discover my Supply Chain Analytics Cheat Sheet
How do we define Supply Chain Analytics?
This is data analytics for Supply Chain Management.
We can use Supply Chain Analytics to define methodologies to extract insights from data across all processes in the value chain.
Let us imagine that our junior data scientist applies for a position in a retail company.
This retailer probably uses
- An ERP to manage procurement, finance and sales
- A Warehouse Management System to manage its distribution centres
- A Transport Management System to manage inbound and outbound freight
We can extract insights from the transactional data stored in these systems.
As a Supply Chain Solution Manager and Data Scientist in the logistics industry, I have used analytics in international projects to design and optimise supply chain solutions.
- 6 years in an international 3PL designing and optimising warehousing and transportation solutions in Asia, Europe and North America
- 3 years as a performance manager using analytics to measure and improve the performance of supply chains in FMCG and luxury group
I shared many of these methodologies and tools in over 75 articles published on Medium.
You can find them in this concise and comprehensive Supply Chain Analytics Cheat Sheet.
Before I proceed, let me explain the content and how I would use it if I were a junior data scientist.
How do you use the Supply Chain Analytics Cheat Sheet?
Ask yourself what kind of problem you want (and can) resolve for the company you want to work for.
Whether you want to reduce distribution costs, minimise environmental impacts or maximise profitability, you will find the answers to your questions here.
How Can Analytics Help Improve Profitability?
Data Analytics to Boost Business Profitability
The first section of the cheat sheet is about data analytics for Business Strategy.
It includes practical case studies on how to use data to support business executives in their strategic decision-making.
For instance, the series of articles, Business Planning with Python, is based on a real example of a business managed by my friend.
My friend: “We have to refuse orders as we don’t have enough cash to pay suppliers for stock replenishment.”
You will discover how I built a simulation model to help him understand weaknesses in his value chain and uncover growth opportunities.
When your insights help to improve profitability, you will attract the attention of recruiters!
These articles illustrate how you can add value to small, medium and large business owners.
What About Optimizing Supply Chain Operations?
Supply Chain Analytics for Logistics Operations
Having spent years designing, monitoring, and optimising supply chain solutions, I have many examples of projects covering multiple industries.
They are compiled in this category, focusing on warehousing and transportation operations.
In this section, most case studies are based on an actual reengineering project I have conducted in Asia and Europe.
Country Manager: “Samir, we need to reduce warehousing costs by 15% if we want to renew the contract with the retail company XXX.”
They focus on optimising a specific process in a warehouse (order preparation, value-added services) or transportation operations (routing, scheduling).
You can easily find case studies to apply the solutions presented.
Go to the nearest warehouse and ask: ‘What are your problems?’ You can be sure they will find some for you.
Here’s how to get started:
- Review the case studies to understand the problem and the solution
- Pull the source code from my GitHub repository
- Search for a similar problem in the company you target
- Adapt the code to build a solution to your specific problem
The code is usually a simple Python script or a Jupyter notebook that can be easily adapted.
This category includes solutions for route planning, warehouse layout optimisation, process scheduling and many other topics related to what happens inside a warehouse or with a truck.
What if you want to have a greater impact? Focus on a flow optimization.
Data Analytics for Supply Chain Optimisation
The main driver of the reengineering projects I have conducted was cost.
Usually, customers tracked logistics costs, i.e. the percentage of turnover spent on logistics operations.
Therefore, we needed to find solutions (as a third-party logistics service provider) to reduce this percentage without impacting our profitability.
What if we delivered to the U.S. East Coast from a warehouse in Charlotte?
The solutions presented in the previous section are too localised (i.e. focusing on a limited scope).
We need to take a step back and consider flow optimisation.
These operational case studies focus on the optimisation of goods flow using
- Replenishment rules and forecasting algorithms to optimise inventory
- Linear/Non-Linear programming to match the supply with demand at the lowest cost
- Statistical tools for diagnosis and improvement of specific processes
For some case studies, I have deployed the models in a web application developed for my startup, LogiGreen.
The demo version is publicly available for you to test the models; more information here.
We covered the cost part so far.
What about sustainability?
If you want to show that you can support companies in their green transformation, I have some examples for you.
Supply Chain Analytics for Sustainability
Since my first project focused on sustainability, I was convinced that green transformation was similar to supply chain optimisation.
The only difference is in how to define the objective function (from minimising cost to environmental footprint).
Therefore, this approach is used in 17 examples of optimisation solutions to minimise CO2 emissions or resource usage.
I also decided to cover the reporting side of sustainability with analytics for Life Cycle Assessment, CO2 emissions calculations or ESG reporting.
These solutions have been the basis of super app developed by my startup to support medium and large companies in their transformation.
These are the solutions I needed while in charge of sustainability roadmaps in large companies.
I propose them via my startup LogiGreen, and you can build similar ones for your next employer!
If you need support getting started with sustainability projects, you’re covered.
Have you heard about the revolution of Agentic AI?
AI Agents for Workflow Automation
A major cost reduction lever for companies is the automation of manual tasks.
Due to a lack of maturity of their systems, many operations still rely on manual processes to manage transactional data.
![Annotated screenshot showing the layout of an invoice with fields like invoice number, zip code, and date. Includes CSS selectors for each field (e.g., span[class=”invoice-number”]) to automate data extraction using tools like Selenium or BeautifulSoup in Python. Highlights invoice line items with item codes, quantities, and total prices.](https://miro.medium.com/v2/resize:fit:1400/format:webp/0*j4-erVbK0dAMuhcD.png)
This is why, in LogiGreen, we implement AI-powered workflow automation in small and medium-sized companies.
In the section Workflow Automation, I share many examples of analytics and AI solutions to automate
- Data extraction from images, emails and PDFs
- Automated reporting using AI agents
- Automated audits for Corporate Sustainability Reporting
Most of these automation solutions use a Large Language Model to interact with data and users.
The great news for our junior data scientists is that they are designed with a low-code solution called n8n.
For instance, the workflow above is to extract order information from an email automatically.
It is an open-source solution that you can deploy on your VPS for free.
You can then use the templates shared in my n8n creator profile; they are ready to be deployed in your instance (also for free).
They all include a detailed presentation and a complete YouTube tutorial, in which I guide you step-by-step to understand how I built it and how you can use it for your automation.
Don’t hesitate to try them! It takes less than 30 minutes to set up everything.
What’s next?
We have covered all the Supply Chain Analytics Cheat Sheet sections.
How to Start Building Your Portfolio?
Let us support that you are this junior data scientist aiming to join a major retailer’s Supply Chain Analytics team.
We will help you find a project that showcases how your skills can help retailers improve their service and reduce costs.
What do you need?
- A Gmail account so you can run Python on notebooks for free with Google Colab (in case you don’t have Python on your machine)
- Your brain and the Supply Chain Analytics Cheat Sheet
Advice 1: Start with a Simple Project
For most companies, data maturity in supply chain departments is very low.
That means the implementation of advanced (and complex) algorithms can be very challenging.
Therefore, I would focus on:
- Delivering business value (visibility, insights, diagnostics)
- Smooth user experience of your product or analysis
Let’s start with a simple solution that impacts.
I would pick the topic of ABC Analysis and Product Segmentation.
This article provides multiple examples of analysis to segment products based on their demand variability and contribution to the turnover.
The article includes a link to a GitHub repository with a Jupyter Notebook containing all the necessary code.
It’s a great way to impact with simple data analysis and an opportunity to learn how to
- Clean sales data to extract insights
- Visualise key performance indicators followed by operations
For more information about this solution,
Advice 2: Add Business Value
My articles always use generic dummy data to feed the algorithms and visuals generated.
You can enrich this data by adapting it to the industry you’re targeting.
- Fashion retailers usually have seasonality and complex master data
- Cosmetics product categories are an essential demand driver that can affect the results of your forecast engine
Before jumping into the code, show that you can take ownership of the case study and adapt it to your vision of the problem to solve.
Advice 3: Code Refactoring and Packaging
My GitHub code is mainly in the form of Jupyter Notebooks or standalone Python scripts.
Basically, it’s far from being ready for production :)
This is an excellent opportunity for our junior data scientist to show that he can package the code into an API or even build a web application around it.
Indeed, data scientists are currently expected to ship their models in a form ready for productisation.
Consider learning about script packaging, Docker containerization, and API development.
Advice 4: Improve the UI and add insights
Remember, your skills will be judged by the impact of the analytics products you design and deploy.
Therefore, do not hesitate to improve the outputs and insights of the models shared in my cheat sheet.
It is an excellent opportunity to ask your colleagues in supply chain operations how these tools can support them.
- What KPIs are they tracking?
- What kind of insights do they lack to pilot their operations?
From here, this case study is yours to make your own.
If you follow these steps, your portfolio will not be a copy of my GitHub repository but a reflection of your skills and how you can impact businesses.
This is precisely what I did when I built the demo version of the LogiGreen Apps.
The demo version is publicly available for you to test the models and get inspiration: more information here.
I’m looking forward to seeing your version of it!
Now, it’s your turn to build solutions that will impact!
If you need a more detailed presentation of the cheat sheet, check out this short YouTube tutorial.
Call to Action: Start your Portfolio Today!
I hope this brief introduction to the cheat sheet has helped clarify how you can start building your analytics portfolio.
Do not hesitate to bookmark this cheat sheet.
I will update it each time new content is published.
I am here to provide support to the community.
I want to use this article and the YouTube video as a forum to collect your feedback or questions.
Feel free to use the comment section to ask questions!
If you have used any case studies for some of your projects, I would be happy to learn more about the results.
About Me
Let’s connect on Linkedin and Twitter; I am a Supply Chain Engineer using data analytics to improve Logistics operations and reduce costs.
For consulting or advice on analytics and sustainable Supply Chain transformation, feel free to contact me via Logigreen Consulting.
