Create a Sustainable Supply Chain Optimization Web App

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Sustainable supply chain optimization is an approach to network design that combines cost-effectiveness with environmental responsibility.

It highlights the complexities that arise when businesses attempt to reconcile environmental considerations with profit objectives.

This topic is increasingly relevant as organizations are pressured to reduce their carbon footprint by reshaping their supply chain networks.

As a Data Scientist, how can you help your organization reach its sustainability targets and improve its ESG score?

Unlike traditional models prioritising outsourcing to low-cost regions, there’s a noticeable shift towards localizing production in environmentally efficient facilities.

However, balancing cost efficiency with CO2 emission reduction is a complex task that requires careful planning and strategic decision-making.

In this article, we introduce an application designed to facilitate data-driven decision-making for optimizing supply chain networks sustainably.

Summary
I. Introduction
II. Sustainable Supply Chain Optimization
  1. The Challenges of Sustainability
  2. The Support of Data Analytics
III. Overview of the Sustainable Supply Chain Optimization App
  1. Purpose and Functionality
  2. Initial Step: Data Input
  3. Second Step: Data Visualization
  4. Third Step: Selecting the Objective Function
  5. Final Step: Visualize the Results
IV. Introduction to VIKTOR
  1. Key Features of VIKTOR
  2. Benefits to Supply Chain Data Scientists
V. Conclusion

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Sustainable Supply Chain Network Design with Python

The idea is to leverage linear programming capabilities to meet global demands while minimizing costs, CO2 emissions and resource consumption.

In the following sections, we will explore sustainable supply chain optimization concepts and the need to integrate sustainability into strategic decisions.

Additionally, you will get a comprehensive overview of the application using an actual example with the sample dataset included in the app.

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Sustainable Supply Chain Optimization

This network design approach ties together environmental responsibility and supply chain efficiency.

Can we find the balance between cost-effectiveness and sustainability?

This is the combination of two concepts I shared in previous articles

• Sustainable Sourcing: integrating social and environmental performance factors when selecting suppliers
• Supply Chain Optimization: design optimal networks to match supply and demand at the lowest cost

What is blocking your company’s green transformation?

The Challenges of Sustainability

The transition towards sustainable supply chain optimization poses a unique set of challenges.

The core complexity lies in aligning efficiency and cost-effectiveness with environmental preservation.

If you produce overseas in countries with low labour and production costs

• You minimize the total cost of production
• You increase the environmental impact with emissions due to transportation and low-efficiency plants.

Then we just have to localize in green facilities!

If you produce in local green facilities

• You maximize the costs because of high labour costs and CAPEX for green equipment that minimizes CO2 emissions and resource usage.
• You reduce the environmental footprint by cutting transportation and using high-end manufacturing installations.

This application will provide you with different scenarios to help you to balance these different constraints.

Use data to support decision-making.

The Support of Data Analytics

In the previous articles, we discovered that linear programming can be key in optimizing the flows between factories and distribution centres.

These models can help you automate in-depth analyses of cost parameters (fixed, variable and transportation) and footprint metrics to find the right balance to satisfy business objectives.

From an algorithmic point of view, you have a set of external parameters

• Demand: demand per market (Units/Month)
• Production capacity per location: high capacity plant (XX Units/Month), low capacity plant (YY Units/Month)
• Environmental footprint: CO2 emissions (kgCO2eq/Unit), resources consumption (L/Unit) or (MJ/Unit) and waste generation (kg/Unit)
• Costs: fixed cost per facility ($/Month) and variable cost per unit ($/Unit)

What are the constraints?

• Constraint 1: Number of units produced ≥ Total Demand
• Constraint 2: CO2 emissions ≤ XX (kgCO2eq/Month)

The algorithm will then select a set of manufacturing locations to open

• A variable is defined for each potential site: (India, Low Cap) = [0 or 1]
• If the value is 1, the location is open and can produce up to its capacity

Based on the objective metric defined by the user, the model can propose the optimal set of boolean values that will minimize this metric.

You can find other applications of data analytics for supply chain sustainability in the video shared below,

Now, it’s clear that sustainability is essential.

Let’s have a look at the tool now.

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Overview of the Sustainable Supply Chain Optimization App

Purpose and Functionality

The primary objective is to provide an interactive platform for supply chain engineers to simulate and evaluate different network design strategies.

It takes a single Excel file with several sheets as input and provides access to the results of multiple simulation scenarios.

If you don’t have data, a sample file is available in the app to test it.

You can try it here

• Sustainable Supply Chain Optimization Application

Initial Step: Data Input

Users can input their data or use a pre-loaded dataset that includes information related to their market demand and manufacturing facilities.

Second Step: Data Visualization

Visualize the different parameters of your model based on the data included in the uploaded file.

🛍 ️ Market DemandIn the location included in your dataset, you have customers (or stores) with a monthly demand in Units/Month.

🏭 Market SupplyIn the location included in your dataset, you have potential manufacturing sites (low and high-capacity plants) with a maximum monthly production in Units/Month.

⚡ Energy UsageThe energy consumed to produce a single unit (MJ/Unit) for each production location.

🗑 ️ Waste GenerationThe amount of waste generated to produce a single unit (Kg/Unit) for each production location.

🚰 Water Usage For each production location, the amount of water used to produce a single unit (L/Unit).

🌲 CO2 EmissionsFor each production location, the quantity of CO2 emitted per unit produced (Kg CO2eq/Unit).

How can I collect these data for my company?

There are multiple sources to gather these input parameters:

• What is a Life Cycle Assessment? LCA: use data analytics to evaluate the environmental impacts of a product over its entire life cycle from production to disposal.
• Supply Chain Sustainability Reporting with Python: 4 steps to build an ESG reporting focusing on CO2 emissions of your Distribution Network

The tool will help you decide where to set up factories to meet demand from all your markets, considering transportation, production costs, and environmental aspects.

What do you want to achieve?

Third Step: Selecting the Objective Function

This leads to informed strategic decisions that improve the efficiency and sustainability of your supply chain.

Users can select among four objective functions,

💰 Production Costs Minimize the overall cost of producing and shipping products to different markets ($/Unit)

🚰 Water Usage Minimize the amount of water used per unit produced (L/Unit)

⚡ Energy Usage Minimize the amount of energy used per unit produced (MJ/Unit)

🌲 CO2 Emissions Minimize the CO2 emissions per unit produced and delivered (kgCO2eq/Unit)

The app automatically returns the results,

Final Step: Visualize the Results

The application provides a comprehensive overview of the results for each scenario, using a detailed breakdown of costs and environmental impacts.

A Sankey chart helps you to trace the flow of goods from production locations to their respective markets.

In the example above,

• Brazil produces 15,000 units/month: 1,450 units for its local market and the rest for the USA.
• The USA, GERMANY and JAPAN are entirely relying on imports

Decision-makers can take a hands-on approach to their network design.

Using data-fueled prescriptions, they will understand the impacts of each objective metric (CO2, Water, Energy, …).

For example, the visual above can raise the following questions

• Q1: Can we afford a 100% cost increase to minimize CO2 emissions?
• Q2: Why is it cheaper to minimize water usage than CO2 emissions?
• Q3: Would it be impactful to find green manufacturing sites in India?

To answer question Q3, let’s have a look at the visual below

As we can see on the right, most emissions per market come from transporting goods from manufacturing plants.

Thus, even with the greenest equipment in India, we would still have high emissions because of transportation.

If you need a short guide on how to use the app, check this tutorial 👇

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Introduction to VIKTOR

VIKTOR is an intuitive platform designed to streamline the creation of engineering projects.

It offers powerful tools for quick deployments of Python-based algorithms.

Key Features of VIKTOR

VIKTOR stands out with its ability to facilitate rapid web application development, testing, and deployment.

My first application was a simple tool to automate ABC Analysis.

After several easy-to-follow steps, I deployed my Python model with an intuitive user web interface to show Pareto and ABC charts.

Try the application,

Do you want to deploy your model? Try VIKTOR.

Benefits to Supply Chain Data Scientists

VIKTOR offers data scientists and analysts a user-friendly platform that integrates well with existing workflows and enhances productivity and efficiency.

For this application, I started with a model I shared on my Github (Link) related to the article about Supply Chain Optimization.

In the tutorial "Getting Started" (documentation), you can find all the different steps needed to deploy a simple app that can

• Take user inputs like Excel files and parameter selection to run the optimization model.
• Show dynamic visuals using Plotly
• Export Word and PDF reports (ABC Analysis App)

Now, I can share this model easily with users who need to gain programming skills to run a Python script.

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Conclusion

Businesses need to respond more effectively to the growing environmental, social, and governance (ESG) demands from stakeholders and regulatory bodies.

This simple prototype, deployed using VIKTOR, can help boost their transition towards green supply chains.

It can be easily improved by adding more advanced functionalities such as,

• Constraints on Maximum Resource Usage or CO2 Emissions
• Introduce variability in the demand to build a robust network
• Visualize the efficiency of the manufacturing footprint (Maximum Production Output/Capacity)

I invite you to explore this application, leveraging its capabilities to test different optimisation scenarios using your dataset.

Have you heard about the global roadmap for a sustainable future?

The Sustainable Development Goals (SDGs) are a set of 17 objectives established by the United Nations to address global challenges.

Dive into my recent insights about how Data Analytics can support the United Nations’ Sustainable Development Goals

What are the Sustainable Development Goals? (SDGs)

About Me

Let’s connect on Linkedin and Twitter. I am a Supply Chain Engineer who uses 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.

Samir Saci | Data Science & Productivity

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References

• Sustainable Supply Chain Network Optimization Application, Samir Saci, Application
• Product Segmentation & ABC Analysis, Samir Saci, Application