Data Science to Reach Carbone Neutrality
How can you use data science to support your organization's sustainability roadmap?
Carbon neutrality is achieved when an entity balances its greenhouse gas emissions with an equivalent offset, resulting in net-zero emissions.
This entity could be an individual, your organization or even an entire country.
Because of the increasing concerns about climate change and global warming, achieving carbon neutrality has become a strategic goal for many companies.
As a data scientist and analytics manager, how can you support your organization?
In this article, we will explore the concept of carbon neutrality to understand its implications and the strategies for achieving it.
I will use the hypothetical example of a fashion retailer implementing a sustainability roadmap to meet shareholders' expectations.
We will focus on the pivotal role of data analytics teams in supporting the implementation of sustainability initiatives.
🙌TL;DR
- Data analytics can become a strategic asset in reaching carbon neutrality.
- Carbon neutrality requires measuring and reducing CO2 emissions along a company's value chain. This involves designing and implementing initiatives to optimize processes.
- This can be done with the support of advanced analytics, which collect, process, and exploit data for descriptive, diagnostic, and prescriptive analytics.
- To illustrate my point, I used the examples of Green Inventor Management, Circular Economies simulation and network design to prove that we (analytics experts) support decarbonization.
Sustainability Roadmap of a Fashion Retailer
Exploit Analytics Ressources to Support the Transformation
Let’s assume you are the Data Science Manager in the Supply Chain department of a global fashion retailer.
This fast fashion retail group sells garments, bags and accessories.
Your team is in charge of the design and implementation of analytics products to support international operations:
- There are 500+ stores on 4 continents that are delivered from local warehouses.
- Factories are replenishing these warehouses with finished goods
- A circular economy model has been implemented to collect and recycle used items.
You have access to multiple systems managing the supply chain.
The organisation uses cloud solutions with a data warehouse connected to
- Enterprise Ressource Planning (ERP) managing production, procurement, finance and store operations
- Warehouse Management Systems (WMS) managing warehouse operations to receive products from factories and deliver stores
- Transportation Management Systems managing transportation operations for warehouse and store replenishment
Thus, your team can access transactional data covering the entire value chain from raw material extraction to store delivery.
How can you exploit this data to support the decarbonization of your supply chain?
Data Science to Support a Sustainability Roadmap
As the company must comply with the United Nations Sustainable Development Goals, the sustainability team is preparing a roadmap for carbon reduction.
The Department of Sustainable Development has requested the support of several teams to reduce the company’s environmental impact.
In the following sections, we will explore several examples of analytics products you deploy to support your organization's decarbonization initiatives.
🏫 Discover 70+ case studies using data analytics for supply chain sustainability🌳and business optimization 🏪 in this: Cheat Sheet
Understanding Carbon Neutrality
Start by Measuring the Current Environmental Impact
If you can’t measure it, you can’t manage it.
— W. Edwards Deming
They need your support to calculate the baseline of emissions.
For instance, the company emitted 150k Tons of CO2eq in 2022.
Therefore, your team can implement an automated solution to perform Life Cycle Assessments.
A life cycle assessment (LCA) is a data-driven methodology that evaluates the environmental impacts of a product, from raw material extraction to disposal.
The idea is to include the impact of every process on
- Natural resources (kg) and energy consumption (MJ)
- The amount of waste generated (kg)
- Pollutants and CO2 emissions (kg CO2eq)
As a concrete example, we can assume that you want to estimate the environmental impact of all t-shirts sold in Raza stores.
To cover the entire value chain, you collect relevant data by considering
- Cotton sourcing with data from suppliers in flat Excel files
- Manufacturing Processes using Production Management Systems and utilities bills
- Freight data using Transportation Management Systems and Forwarders APIs
These multiple data sources can be combined in a single data lake for processing and harmonization using Business Intelligence tools and methodologies.
You can now provide a baseline of the current emissions to start building a roadmap for decarbonization.
How can we use data to transform our business operations?
Let’s define carbon neutrality and explore several analytics solutions that can be deployed to support the transformation.
What is Carbon Neutrality?
In simple terms, carbon neutrality implies offsetting this amount of CO2 to reach a zero-emission impact on the environment.
This can be achieved by implementing initiatives to reduce the footprint of the current manufacturing and logistics processes.
- Renewable Energy Adoption
Raza can support the transition to 100% renewable energy sources for the factories, distribution centres and stores. - Sustainable Packaging
Raza can promote the usage of recycled and biodegradable packaging - Circular Economy Integration
Raza can cut emissions from raw material extraction and production by introducing a clothing recycling program or a rental business model. - Efficient Transportation
Raza opts for electric delivery trucks and implements optimized delivery routes to reduce the footprint.
I would classify these initiatives into three categories,
Category 1: Investment-led Initiatives to improve the efficiency of the equipment and resources usage.
- Sustainable Raw Materials: usage of recycled materials and sustainable packaging that consume less raw materials and reduce waste
- Green Equipment: electric vehicles, high-efficiency equipment (manufacturing, warehousing) that require high investments
Depending on the types of products used or the industry, these two initiatives will reduce but not eliminate the emissions.
Category 2: Offsetting Initiatives: For the remaining emissions that can’t be reduced directly, Raza can invest in projects offsetting CO2.
- Reforestation projects
Planting of trees that absorb equivalent amounts of CO2 from the atmosphere. - Sustainable Aviation Fuel (SAF)
SAF is a blend of conventional jet fuel and biofuel that reduces emissions by 30%.
Some companies propose to buy SAF and inject it into the market so the emissions cuts can be used to offset remaining emissions. - Renewable Energy Surplus
Solar panels and wind turbines can produce more electricity than is consumed, and the surplus can be sold back to the grid.
These initiatives require high investments, which is the main reason why carbon neutrality can be considered only for companies with high margins.
Hopefully, data analytics and operations research can help us design solutions to make this concept more universal with the last category.
Category 3: Supply Chain Flow Optimization
The idea is to cut emissions with process(es) change without additional investments.
1) Start by auditing the process
For instance, the team would like to focus on the CO2 emissions of trucks delivering stores from the local warehouse.
2) Build a simulation model of the actual operations
As a data scientist, you can partner with process engineers to build a digital replica of the current operations.
This model can take actual store orders as input data, simulate delivery routes and estimate the CO2 emissions.
- Green Inventory Management: Reduce the environmental footprint by optimizing the delivery frequency
- Supply Chain Network Optimization: Select the production and warehouse locations that minimize the CO2 emissions of production and transportation
These are my favourite initiatives as they rely on operational knowledge and advanced analytics to cut emissions.
Because they focus on optimising flows, they can also generate cost savings.
If you are working on a sustainability roadmap, these initiatives can be used to get emissions cuts at a low cost.
💡 You can find detailed case studies in this blog,
- Green Inventory Management — Case Study, Samir Saci
- How Sustainable is Your Circular Economy?, Samir Saci
- Create a Sustainable Supply Chain Optimization Web App, Samir Saci
In the next section, we will focus on Supply Chain Optimization solutions supported by advanced analytics.
Supply Chain Analytics: A Key to Carbon Neutrality for Low-Margin Businesses
Carbon Neutrality Matters for All Companies
Sustainability is a strategic imperative for companies that want to stay compliant and keep a competitive advantage in their markets.
However, this looks like an additional challenge for companies already struggling to keep their operating margin.
We cannot propose initiatives that increase the logistics cost and kill a company's operating margin if it is struggling to reach a 5% margin.
Economic Benefits and Emissions Cuts
Supply Chain Analytics provides tools to use the data created in IT systems (Warehouses, Transportation, Factories) to answer questions and support decision-making.
How can we combine cost efficiency with footprint reduction?
As a Supply Chain Engineer, I have used advanced analytics solutions to support continuous improvement initiatives in different geographies across multiple industries.
Although the goal was always cost reduction, most of the time it was also reducing the environmental footprint of logistic operations.
💡 How to use data analytics for sustainability?
- Data Analytics for Supply Chain Sustainability, Samir Saci
Let me illustrate this idea with two examples.
Example 1: Green Inventory Management
Green inventory management can be defined as managing inventory in an environmentally sustainable way.
Let’s come back to the example of our fashion retailer.
When a store needs to be replenished, an order is created in the system.
This order launches a set of procedures to prepare, load and ship this order to the store.
As a former logistic engineer, I am aware of the tremendous impact of the ordering policy on the efficiency of the distribution process.
If a store orders too frequently,
- Quantities per order are very low which results in low productivity in the warehouse, bad truck filling rate and an increase in packing material usage
- The number of deliveries with small trucks is multiplied which results in a higher amount of CO2 emissions
In order to prove my point, I have designed a simulation model that replicates the distribution operations of a distribution center that replenishes 10 stores.
I have run several with different order frequencies to see the impact,
- Packing Material and Carton Usage (kg)
- Truck Fill Rate (%)
- CO2 Emissions
The results of this simulation show a clear trend when we increase the review period (reduce the frequency).
The emissions are dropping until we reach the local minimum for a review period of 7 days.
The trend is different for the carton usage as the quantity keeps decreasing even after 7 days.
This also results in
- Decrease in transportation costs as we use fewer trucks
- Lower packaging costs as we use fewer cartons
That means we can cut emissions and reduce logistics by only adapting our store ordering process without heavy investments.
Example 2: Prescriptive Analytics for Supply Chain Network Design
Where should we locate our factories to minimize the CO2 emissions of our Supply Chain Network?
Supply chain optimization makes the best use of linear programming to find an optimal combination of factories and distribution centres to match supply and demand.
Original Problem Statement
- Market Demand Constraints: minimal quantity to ship per market location (Units)
- Supply Capacity Constraints: list of manufacturing locations with production capacity (Units), costs (fixed, variable and transportation) and environmental footprint (production and transportation)
- Objective function: minimize the total cost of producing and delivering goods
The final result with be a set of factories that minimizes the cost of producing and delivering products to customers.
For the sake of footprint reduction, we can
- Adapt the objective function to minimize the emissions
- Add constraints on maximum footprint per item produced
This provides a network solution that can drastically cut emissions by balancing transportation (distance factory to market) and production (country of production, equipment quality) emissions.
💡 What are the advantages?
- Combining the two indicators in your objective function can also result in cost reduction.
- It does not require additional CAPEX as it relies on the same set of manufacturing locations available.
These two examples have the common objective of using data to maximize resource usage.
- Producing close to the markets to reduce the transportation distance
- Adapting the order frequency to optimize the quantity per shipment
For a company that struggles with profitability, this is the approach that can provide results without endangering the business model.
Conclusion
Carbon neutrality represents a vital step towards a more sustainable and resilient future.
Achieving it requires concerted efforts across all aspects of a business, with supply chains playing a critical role.
While the journey towards carbon neutrality may be challenging, the potential benefits in terms of sustainability, business growth, and regulatory compliance make it a goal worth striving for.
By integrating data science and supply chain analytics, organizations can make more informed decisions and take significant strides towards achieving this sustainability goal.
Hence, it’s time for businesses to understand, embrace, and actively work towards carbon neutrality.
The future of our planet depends on it.
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.
If you need consulting or advice for your supply chain transformation, contact me via Logigreen Consulting.
If you are interested in Data Analytics and Supply Chain, have a look at my website
