Create a Sustainable Supply Chain Optimization Web App
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. ConclusionSustainable 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.
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.
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
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.
