22 Impacting Projects to Start Your Data Science for Supply Chain Journey
A list of Data Science for Supply Chain case studies that can be used to develop your skills and quickly impact your organization.
If you want to impact your organization with data science, supply chain management is the best place to start your data science journey.
As a Data Scientist, how can I provide value to my organization?
When I was a Supply Chain Solution Designer, my job was to
- Translate our customer's requirements into actual operational solutions (Retail, E-Commerce, Luxury, FMCG, Automotive)
- Conduct re-engineering studies to improve warehouse operations and optimize transportation networks.
Leveraging data science, simulate supply chain scenarios to improve the performance and reduce costs.
In this article, you can find 22 major Data Science for Supply Chain case studies that can be applied to your operations by following detailed tutorials.
For each example, I share the Python source code with dummy data on GitHub so you can try to adapt the model for your projects.
I. Supply Chain Optimization with Python
Use data to produce and deliver products efficiently
1. What is Supply Chain Analytics?
2. Optimize production planning with Python
3. Supply Chain Optimization with Python
II. Warehouse Operations Optimization with Python
Support logistics operations to receive, store and prepare goods
1. Improve warehouse picking productivity with Python
2. Optimize Workforce Planning Python
3. Lean Six Sigma with Python using the Kruskal Wallis Test
III. Data Visualization with Python
Design interractive visualization to present results
1. Deploy Interactive Dashboard using Python Flask and javascript D3.js
IV. Inventory Management & Demand Forecasting
Forecasting and rules to minimize storage costs and avoid stock-outs
1. Inventory Management for Retail with Python
2. Machine learning for Retail Sales Forecasting with Python
V. Transportation Optimization with Python
Reduce costs by maximizing the efficiency of your routing
1. Transportation Network Analysis with Python using Graph Theory
2. Transportation Network Visualization using Python
VI. Data Science for Supply Chain Sustainability
Use analytics to reduce the environmental footprint of your company
1. Supply Chain Sustainability Reporting with Python
2. How sustainable is your circular economy?
3. What is Green Inventory Management?
4. Sustainable Supply Chain Optimization App
5. Automate ESG reporting with Python
6. Fight Greenwashing with Python
7. Leverage Data Analytics for Sustainability
VII. Generative AI for Supply Chain
How can we use LLM to improve user experience of analytics products?
1. Leveraging LLMs with LangChain for Supply Chain Analytics
2. Automate supply chain analytics with GPTsLet us start with analytics for supply chain optimization.
What is Supply Chain Analytics?
Scope: Supply Chain Optimization
Objective: Use data analytics with Python to improve operational efficiency by enabling data-driven diagnostics and decisions at strategic and operational levels.
A Supply Chain can be defined as several parties exchanging flows of material, information or money resources to fulfil a customer request.
As information plays an important role, Supply chain Analytics has emerged as the methodologies and tools organizations use to get insights from data associated with all processes included in the value chain.
In this article, you will discover the different types of Supply Chain Analytics with Python and understand their impact on the efficiency of your end-to-end operations so you can start your project.
Optimize production planning with Python
Scope: Supply Chain Optimization
Problem Statement: The master production schedule is the main communication tool between the commercial team and production.
Your customers send purchase orders with specific quantities to be delivered at a certain time.
Production planning minimizes the total cost of production by finding a balance between minimizing inventory and maximizing the quantity produced per setup.
Objective: In this article, we will implement optimal production planning using the Wagner-Whitin method with Python.
What is Supply Chain Optimization?
Scope: Supply Chain Optimization
Problem Statement: Supply chain optimization uses data analytics to find an optimal combination of factories and distribution centres to meet customers' demands.
The core structure of many software and solutions in the market is a Linear Programming Model.
Some of these models find the right allocation of factories to meet the demand and minimize the costs, assuming a constant demand.
What happens if the demand is fluctuating?
Your network may lose robustness, especially if your demand is very seasonal (e-commerce, cosmetics, fast fashion).
Objective: In this article, we will build a simple methodology to design a Robust Supply Chain Network using Monte Carlo simulation with Python.
Let us move to warehouse operations.
Improve warehouse picking productivity with Python
Scope: Warehouse Operations
Problem Statement: In a Distribution Center (DC), walking time from one location to another during the picking route can account for 60% to 70% of the operator’s working time.
Objective: How can you use Data Science to increase Warehouse Operators’ productivity by reducing walking distance?
I have written several articles explaining how to use Order Batching, Spatial Clustering and Pathfinding Algorithms to improve picking productivity.
Concept & Libraries Used:
- Create Orders Batch using Python’s Pandas, Numpy
- Spatial Clustering of Picking Location using Python’s Scipy
- Pathfinding for Picking Route design using Google OR
Results: An increase in the picking productivity will lead to cost reductions
Link to the Articles
Optimize Workforce Planning Python
Scope: Warehouse Operations
Problem Statement: What is the minimum number of temporary workers you need to hire to absorb your weekly workload while ensuring employee retention?
Objective: To meet your manager's productivity targets, you must minimize the number of workers hired to handle the workload.
I have written a medium article on using Linear Programming to find the correct number of workers to hire.
Concept & Libraries Used:
- Linear Programming with Python’s PuLP
Results: Calculate the minimal number of workers that respects all the constraints.
Lean Six Sigma with Python using the Kruskal Wallis Test
Scope: Warehouse Operations
Lean Six Sigma (LSS) is based on a stepwise approach to process improvements, following five steps (Define, Measure, Analyze, Improve, and Control).
As a continuous improvement Manager of a Distribution Center (DC) for an iconic Luxury Maison, you want to use this approach to improve the productivity of a specific process.
Does the training have a positive impact on the productivity of operators?
Hypothesis
The training has a positive impact on the productivity of VAS operators.
Experiment
Randomly select operators and measure the time per batch (Time to finish a batch of 30 labels in seconds) to build a sample of 56 records.
Objective: In this article, we will explore how Python can replace Minitab in the Analysis step to test hypotheses and understand what could improve the performance metrics of a specific process.
Do you need to develop interactive visualizations?
Deploy Interactive Dashboard using Python Flask and javascript D3.js
Scope: Visualization & Reporting
A simple and fancy visualization can impact more than a very complex model, especially for a non-technical audience.
Therefore, building your visualization skills is essential to your Supply Chain Data Scientist job.
I have written a medium article on designing fancy visualization using D3.js without prior knowledge of JavaScript (or very light).
Concept & Libraries Used:
- Data Processing with Numpy, Pandas, Flask and D3.js (Link)
Results: A dynamic dashboard that can interact with users to show business insights.
What about inventory management?
Inventory Management for Retail with Python
Scope: Inventory Management
Problem Statement: For most retailers, inventory management systems take a fixed, rule-based approach to forecast and replenishment order management.
Considering the demand distribution, the objective is to build a replenishment policy to minimize your ordering, holding and shortage costs.
Objective: We want to introduce a simple methodology for testing several inventory management rules using a discrete simulation model built with Python.
Machine Learning for Retail Sales Forecasting with Python
Scope: Inventory Management & Demand Forecasting
Problem Statement: Based on the feedback of the last Makridakis Forecasting Competitions, machine learning models can reduce forecasting errors by 20% to 60% compared to benchmark statistical models. (M5 Competition)
Their major advantage is the capacity to include external features that heavily impact the variability of your sales.
For example, e-commerce cosmetics sales are driven by special events (promotions) and how you advertise a reference on the website (first page, second page, …).
Feature engineering is based on analytical concepts and business insights to understand what could drive your sales.
Objective: In this article, we will try to understand the impact of several features on a model’s accuracy using the M5 Forecasting competition dataset.
Let us optimize the road transportation!
Transportation Network Analysis with Python using Graph Theory
Scope: Road Transportation
Objective: Build Visuals to support FTL routing optimization.
Problem Statement: For a retailer, road transportation to deliver stores represents a significant part of the logistics costs.
Companies often conduct route planning optimization studies to reduce these costs and improve the efficiency of their network.
It requires collaboration between continuous improvement engineers and the transportation teams managing daily operations.
Objective: In this article, we use Graph Theory to design visual representations of a transportation network to support solution design.
Transportation Network Visualization with Python
Scope: Transportation Operations
Problem Statement: How do you organize the delivery routes and truck loading to reduce your costs?
Objective: Visualisation and Costing of your Transportation Plan to optimize the loading rate and reduce the costs per ton of your Transportation.
I have written a medium article on processing data and preparing visualization to impact the average cost per ton of your deliveries.
Concept & Libraries Used:
- Data Processing with Numpy, Pandas and visualization with Matplotlib
Results: An Optimized transport plan using larger trucks with a higher loading rate.
Do you want to support the green transformation of your company?
Supply Chain Sustainability Reporting with Python
Scope: Sustainability
Problem Statement: The demand for transparency in sustainable development from investors and customers has grown.
Investors have increasingly emphasised the business’s sustainability when assessing an organisation’s value and resiliency.
Therefore, more organizations are investing resources to build capabilities for sustainability reporting and determine the best strategies for a sustainable supply chain.
Objective: In this article, we will introduce a simple methodology to report the CO2 emissions of your Distribution Network using Python and PowerBI.
How Sustainable is Your Circular Economy?
Scope: Supply Chain Sustainability
Key Skills: Inventory Management, Life Cycle Analysis, CO2 Emissions Calculation, Supply Chain Descriptive Analytics (Data Processing, Visualization and KPI creation)
Problem Statement: A circular economy is an economic model that aims to minimize waste and maximize resource efficiency.
It involves designing products and processes focusing on longevity, reuse, and recycling.
Some companies have implemented a subscription model where customers pay a regular fee to access a product or service for a specific period.
Objective: Use data analytics to simulate the impacts of several circular subscription model scenarios on emissions reductions and water usage of a fast fashion retailer.
What is Green Inventory Management?
Scope: Supply Chain Sustainability
Key Skills: Inventory Management, CO2 EmissionsCalculation, Supply Chain Descriptive Analytics (Data Processing, Visualization and KPI creation)
Problem Statement: Green inventory management can be defined as managing inventory in an environmentally sustainable way.
A distribution network can involve processes and rules to reduce the environmental impact of order transmission, preparation and delivery.
What would be the impact on CO2e emissions if we reduce the frequency of store replenishments?
Objective: Use data analytics to simulate the variation of store replenishment frequency and measure the impact on the overall environmental impact.
Sustainable Supply Chain Optimization Application
Scope: Sustainability
Objective: Help your organization combine sustainable sourcing and supply chain optimization to curb costs and environmental impacts.
I developed an application using the VIKTOR platform to facilitate data-driven decision-making for supply chain sustainability.
For more details,
Automate ESG Reporting with Python
Scope: Sustainability
Objective: Comprehensive and practical environmental, social and governance reporting of a company
Problem Statement: Environmental, Social and Governance (ESG) reporting is a method companies use to disclose their governance structures, societal impacts and ecological footprint.
How can you use data analytics to automate this reporting?
As stakeholders increasingly demand corporate social responsibility (CSR), ESG reporting has become critical to companies’ long-term strategies.
This job requires to collect and process data from multiple sources.
In the article, we provide examples of analytics solutions that you can use to support your ESG reporting 👇
Fight Greenwashing with Python
Scope: Sustainability
Objective: use data analytics to detect greenwashing from large corporations.
Problem Statement: Greenwashing is the practice of making misleading claims about the environmental benefits of a product or a service to communicate a false image of sustainability.
How can we use data to detect frauds?
In this article, we try to understand the different types of greenwashing to explain its manifestations.
We will also introduce data analytics methods to detect and prevent these unethical practices. 👇
Leverage Data Analytics for Sustainability
Sustainability has become a critical aspect of business operations as companies face mounting pressure to address environmental and social issues for their ESG reporting.
Scope: Sustainability
Experts exposed the barriers that companies face in their pursuit of sustainability, focusing on four “hidden enemies”:
- Structure and Governance: Siloed sustainability limits influence.
- Processes and Metrics: Unsustainable metrics hinder progress.
- Culture and Leadership: Old mindsets challenge transformation.
- Methods and Skills: Traditional tools obstruct change.
Problem Statement: As a data scientist, analyst, or continuous improvement engineer, how can your company boost its green business transformation?
Objective:
Explore how data analytics can help to overcome these challenges by focusing on the four “hidden enemies” of your Supply Chain green transformation.
Have you heard about Generative AI?
Leveraging LLMs with LangChain for Supply Chain Analytics
Scope: Generative AI
Objective: Master Langchain with OpenAI’s GPT models and build the ultimate Supply Chain Control Tower.
Create a LangChain agent connected to a local database to answer operational questions using data.
Automate supply chain analytics with GPTs
Scope: Generative AI
OpenAI introduced a new feature allowing users to create custom versions of ChatGPT tailored for specific purposes.
This is an opportunity to easily create and deploy an agent to automate Pareto and ABC analyses.
Objective: Introducing “The Supply Chain Analyst”, a custom GPT agent designed to automate supply chain analytics tasks and interact with users using natural language.
Conclusion
These examples can be directly applied to your operations using your data sets to provide insights that will impact your organization quickly.
Feel free to leave questions in the comment section.
You can follow me on Medium for more articles related to Data Science for Supply Chain Optimization.
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.
If you are interested in Data Analytics and Supply Chain, look at my website.
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References
[0] Samir Saci, My GitHub Portfolio, Link
