What is a Life Cycle Assessment? LCA

Perform Life Cycle Assessment with Python to help businesses evaluate a product's environmental impacts over its entire life cycle.

Samir Saci
Towards Data Science
13 min readJan 18, 2023

A flowchart illustrating the life cycle of a fast-fashion retail product. It begins with raw material extraction (represented by a cloud), followed by production in a factory, transportation by ship, storage in a warehouse, delivery by truck, and sale in a retail store. The cycle ends with product disposal. The chart represents the entire life cycle of a product from production to disposal, as part of a life cycle assessment (LCA) to evaluate environmental impacts.
What is a Life Cycle Assessment? — (Image by Author)

As consumers become increasingly environmentally conscious, businesses must prioritize sustainability to remain competitive.

Life cycle assessment (LCA) evaluates a product's environmental impacts throughout its life cycle, from raw material extraction to disposal.

How much water is needed to produce my T-shirt?

The life cycle assessment aims to identify and quantify a product's environmental impacts for reporting purposes and to support sustainability initiatives.

How to use data analytics to estimate the environmental impact of the cheap t-shirt you bought in Zara?

You need data from multiple sources to track the impact of your products along the value chain until their end of life.

A flowchart showing the resources used and waste generated in the life cycle of a product. The top row illustrates the use of resources like water, energy, and materials at each stage: production, transportation, storage, and retail. The bottom row highlights the waste and CO2 emissions generated at each stage, from the factory to the store. The chart emphasizes how resource use and emissions occur at each step of the product’s life cycle.
Resources Usage and CO2 Emissions along the Value Chain — (Image by Author)

This article shows how data analytics can support Life Cycle Assessment (LCA) by extracting and processing data from multiple systems to perform diagnostics and simulate scenarios.

I. Assessment in four steps
1. Goal and scope definition
Define the goals and scope of this assessment.
2. Inventory Analysis
Gather data on the materials, energy, and other resources used
3. Interpretation and evaluation
Assessing the overall environmental performance of the product
II. Life Cycle Assessment for Advanced Reporting
1. ESG reporting
Report the Environmental, Social and Governance of your company
2. Data Analytics to Fight GreenWashing
Detect false claims of sustainability using public data
3. Business Intelligence for LCA
Methodologies to collect and process from multiple sources
III. Next Steps
1. Simulate different scenarios with a Digital Twin
What is the impact of localizing your production on your LCA?
2. Conduct Further Analysis with Brightway2
Open source librairies with publicly available databases
3. Sustainable Supply Chain App
Design the optimal manufacturing facilities to minimize CO2 emissions
4. Automate Data Extraction from Excel files using Python
Create a python script to extract data from utilities bills

Assessment in four steps

We assume you are a data scientist in a Fast Fashion retail company working with the sustainability department to implement automated sustainability reporting.

What do we want to achieve with Life Cycle Assessment?

Step 1: Goal and scope definition

To initiate our Life Cycle Assessment (LCA), we need to define the goals and scope of this assessment.

What is the environmental impact of producing and selling a t-shirt in a fast-fashion retail company?

This includes identifying the product being studied, the environmental impacts of interest, and the functional unit (the unit of measurement used to compare different products or services).

A diagram outlining the goal and scope definition for a Life Cycle Assessment (LCA) of a T-shirt in a fast-fashion retail company. The life cycle stages are depicted, from raw materials to disposal. Geographic boundaries cover multiple countries (e.g., India, France, Germany, Spain, Italy, Belgium). The product studied is a T-shirt (sizes S, M, XL), and the functional unit is defined as one sale unit (one T-shirt).
Scope of Life Cycle Assessment includes time frame, life cycle stages, geographic boundaries and functional unit — (Image by Author)
  1. Identify the product being evaluated and its intended use
    Example: T-shirt Regular Fit including all sizes
  2. Identify the specific life cycle stages of the product to be included in the assessment: from raw material extraction to disposal.
  3. Specify the geographic boundaries of your assessment: the locations of the production facilities, transportation routes, and final disposal.
    Raw materials extraction and production in India
    Storage, store delivery and disposal in Europe
  4. Identify the time frame of the assessment: starting and ending dates of the assessment period.
    From January 1st 2021 to January 1st 2022
  5. Define the functional unit, a standard measure to compare different products' environmental impacts.
    1 unit of sales: a single T-shirt

Ensure that the scope is agreed upon by all stakeholders involved in the assessment.

This ensures that the Life Cycle Assessment results are meaningful and representative and will not be challenged.

Extract and process data from systems to feed your LCA report — https://samirsaci.com
Extract and process data from systems to feed your LCA report — (Image by Author)

The product will be linked to one (or several) SKU code(s) in your different systems (ERP, WMS, TMS)

  • You may have several codes for the different sizes and colours of t-shirts.
  • Codes can differ from one system to another (SKU Production Code, SKU in Sales Packaging, ..): Ensure you have consistent master data to track your items along the value chain.

From raw materials to finished products, your value chain involves warehouses, factories and freight operations.

For each life cycle stage, you may have different data sources/systems

  • Production data can be found in your Production Management Systems or your Enterprise Resources Planning
  • Transportation will be managed by Transportation Management Systems
  • Warehouse Operations data are stored in a Warehouse Management System
  • Sales (and after-sales) data can be extracted from your Point of Sales (POS) and Customer Relationship Management (CRM) systems

Raw materials are sourced in India for manufacturing in Bangladesh and retail in Europe.

Geographic boundaries can be defined by the source system and transaction information

  • Raw material sourcing information can be found in the purchase orders created in the ERP
  • Production locations information in the PMS (Factory, production line)
  • Transportation locations are tracked in the shipment information in your TMS (Origin, Destination, Countries crossed)

Identifying the time frame can be challenging and will impact your assessment results

  • Are we talking about all products sold in the time frame?
    If yes, does that mean we have to look at production, transportation and warehousing activities that occurred before the starting date?
  • Or do we look at the activities in the time frame?
    If yes, let’s look at the impact of production and transportation of products sold after the ending date.

Note that the scope could be more or less detailed according to your needs and the expected initiatives to benefit from the results.

What data do have on hand for this analysis?

Step 2: Inventory Analysis

The second step is to gather data on the materials, energy, and other resources used and the waste generated throughout the product or service's life cycle.

A flowchart showing the environmental impacts at different stages of a product’s life cycle, including “Cultivate,” “Production,” “Transport,” and “Usage.” Each stage highlights resource consumption and emissions, such as water, energy, and CO2 emissions. The diagram visually represents the impacts of cultivation, production, transportation, and usage on the environment.
Inventory Analysis on a Simplified Cycle for a Life Cycle Assessment — (Image by Author)

Raw materials
Your t-shirt is made of 100% cotton grown and processed in India

(Table by Author)

You can now quantify the CO2 emitted and the energy and water used per functional unit.

💡 Impact of Raw Material Cultivation

  • We have 1kg of raw material = 1 functional unit.
  • Quantities of cotton and utilities consumed can be estimated or measured by your suppliers.

What is the impact of raw materials transformation?

Production
The T-shirt is produced in your factories located in India

(Table by Author)

Transportation
Your t-shirt is produced in India and shipped to Europe by sea freight and road,

A table format with data from the inventory analysis of a product’s life cycle. The table lists items such as distance traveled (10,000 km), CO2 emissions from sea transportation (160 g CO2e per T-shirt), energy consumption (150 MJ per T-shirt), and other emissions, including SOx and NOx, measured in grams per T-shirt.
(Table by Author)

💡 Impact of your Logistic Network

  • Sea transportation energy and emissions data are highly variable and depend on the vessel, time of year, and shipping route. Your freight forwarder should validate (or provide) these data.
  • CO2 emissions of transportation can be estimated for the other transportation modes (route, air and rail) like in this example of CO2 emissions reporting methodology
  • For water and electricity consumption, CO2 emissions, and generated waste, warehouse operations can also be considered.

What happens after the T-shirt is sold?

Usage and disposal
Your product is used (usage and disposal) in the market’s country.

The t-shirt is used for two years before being disposed of.

  • Energy consumption for washing and drying: 100 MJ/t-shirt
  • Water consumption for washing and drying: 500 L/t-shirt
  • Emissions from electricity generation: 10 kg CO2e/t-shirt

The t-shirt is disposed of in a landfill in the country where it is sold.

  • Emissions from decomposition: 5 kg CO2e/t-shirt

Great, now we have covered the entire life cycle.

Step 3: Impact assessment

Now that we have gathered data for each step, we can start evaluating the environmental impacts of the functional unit (1 t-shirt here) on

  • Air, water, and soil
  • Human health and ecosystem health
  1. Energy consumption: 870 MJ
    58% consumed during the production
  2. Greenhouse gas emissions: 46 kg CO2e
    With a majority of emissions during production
  3. Water consumption: 3,500 L
    57% consumed during production
  4. Solid waste: 0.5 kg
    generated during production
  5. Air pollution: 0.8 g of SOx and 0.5 g of NOx emissions
    emitted during transportation

The majority of the impacts are generated during production and transportation.

Step 4: Interpretation and evaluation

This is assessing the product's overall environmental performance and identifying areas for improvement and potential mitigation strategies.

A flowchart illustrating the interpretation and evaluation step of a Life Cycle Assessment (LCA) for a T-shirt. The diagram traces the product’s life cycle stages, including material extraction, production, transportation, store sales, usage, and disposal. It also includes metrics for distance traveled (e.g., 10,000 km) and energy consumption (e.g., 500 MJ and 200 MJ). A pie chart visually represents the breakdown of environmental impacts, helping assess the product’s environmental performance.
Interpretation and Evaluation of Life Cycle Assessment in Four Steps — (Image by Author)

Comparison to industry standards
The impact assessment results can be compared to industry standards or benchmarks to see how the t-shirt compares to similar products regarding environmental performance.

Identification of hotspots
The impact assessment results can be used to identify the “hotspots” where the t-shirt has the most significant environmental impact.

In our example, these hotspots are greenhouse gas emissions and energy consumption during production and transportation.

💡 Diagnostic Analytics for automated identification

If you have implemented a data pipeline to gather, process and store data for your inventory, you can implement diagnostic analytics tools and methodologies to automatically

  • Manage several thousand SKUs with different value chains
  • Implement automated rules for monitoring, alerting and root cause analysis to answer questions like
    Why did the CO2 emissions of SKU 132897–98 increase by 20%?

Potential mitigation strategies
Potential mitigation strategies can be developed based on the hotspots identified to reduce the t-shirt's environmental impact.

  • Use renewable energy for production facilities.
  • Localize the production to reduce the transportation distance between factories and markets.
  • Partner with forwarders that use alternative fuels or own eco-efficient fleets to reduce the CO2 emissions per km
  • Conduct studies for Supply Chain Network Optimization focusing on reducing the environmental impact by choosing the suitable suppliers and factory locations

Continuous improvement
The interpretation and evaluation results should be used to continuously improve the product's or process's environmental performance over time.

Of course, you must consider the trade-offs between the environmental impacts and other aspects, such as cost and performance, to reduce the business impacts.

💡 For more details,

Now that you have implemented a process for collecting and processing data to generate insights, you can build sustainability reports.

Life Cycle Assessment for Advanced Reporting

LCA for ESG Reporting

Environmental, Social, and Governance (ESG) reporting is a methodology corporations use to disclose their environmental footprint, governance structures, and societal impacts.

A diagram illustrating the three pillars of ESG (Environmental, Social, and Governance) reporting. The Environmental section includes items like carbon footprint reduction, climate change strategy, and energy efficiency. The Social section lists fair living wages, equal job opportunity, and respecting labor laws. The Governance section covers corporate governance, risk management, compliance, and ethical business practices.
ESG Pillars Presentation — (Image by Author)

As stakeholders increasingly demand corporate social responsibility (CSR), reporting has become critical to companies’ long-term strategies.

How can you support ESG reporting with data analytics?

The core of Environmental (E) reporting lies under the life cycle assessment, as you need to report the impact of your products from raw material sourcing to store delivery.

A visual representation of ESG reporting, with three sections labeled E (Environmental), S (Social), and G (Governance). The Environmental section includes icons representing CO2 reduction and sustainable practices. The Social section focuses on community and workforce representation, while the Governance section highlights corporate governance and business ethics.
Three main pillars of the ESG reporting — (Image by Author)

The indicators under the environmental part depend on parameters that are included in the Life Cycle Assessment.

💡 For more details,

Have you heard about greenwashing?

What Greenwashing Is, and How We Can Use Analytics to Detect It

While you’re trying to accurately report the environmental impacts of your operations, other companies lie.

Use data analytics to challenge sustainability reports.

Some companies make misleading claims about a product's environmental benefits to communicate a false image of sustainability.

A diagram highlighting different types of greenwashing practices. The categories include “Lies,” “Vagueness,” “Proof-less,” “Irrelevance,” and “Trade-off,” each represented with an icon. These terms refer to misleading or false claims made by companies about their environmental practices or products to create an illusion of sustainability.
5 sins of greenwashing — (Image by Author)

This practice of embellishing or hiding falsehoods as companies seek the attention of environmentally conscious consumers challenges organizations and governments.

Can we fight greenwashing with advanced analytics?

A flowchart illustrating the use of data analytics to detect greenwashing. The chart presents various tools and techniques, such as NLP (Natural Language Processing) and data analysis, to analyze environmental claims. The chart includes icons representing analytics methods and highlights the ability to validate or invalidate sustainability reports using data-driven insights.
Advanced Analytics used to detect greenwashing in environmental reports — (Image by Author)

We can combine data science with Life Cycle Assessment methodologies to detect these frauds with

  • Publicly available data: financial and sustainability reports, footprint databases, social media
  • Advanced analytics models like NLP, forecasting or statistical models to detect fraud

💡 For more details,

What are the best practices for collecting and processing data from multiple systems?

Business Intelligence for Life Cycle Assessment

Business Intelligence is a process that leverages software and services to transform data into actionable intelligence supporting decision-making.

A five-step flowchart showing the process for Business Intelligence (BI) in Life Cycle Assessment (LCA). Each step is labeled, starting with “Step 1” (data collection), “Step 2” (data processing), “Step 3” (error checking and validation), “Step 4” (report generation), and “Step 5” (report dissemination). Icons above each step represent the corresponding actions in the BI process.
Business Intelligence process in five steps — (Image by Author)

As your life cycle assessment relies on collecting and processing data from multiple sources, you need a method to implement an automated pipeline.

How can you automate the collection and processing of these data?

Instead of using estimation or average, you can build a data architecture to retrieve updated data and metrics in a data lake.

A diagram showing how a data lake is used for gathering and processing data for Life Cycle Assessment (LCA). Data from various factory systems, production management systems, and suppliers is collected, including metrics like production output, energy and water usage, and emissions. This data is processed and used for reporting via an API or Excel reports, helping assess environmental impacts.
Central Extraction of Data from Multiple Sources — (Image by Author)
  • The system that manages your factory may not provide information on energy, water usage, or waste generation per unit.
    Use external sources like energy bills in Excel files
  • Your suppliers can provide energy and water usage for cotton cultivation in Excel reports that will feed your reporting
  • Some freight forwarders have API(s) to extract routing information, emissions and fuel consumption per shipment

This architecture will reduce the amount of manual work and improve the accuracy of your reporting by using up-to-date parameters and input data.

How can use analytics solutions to create a central source of truth with harmonized data?

Business Intelligence tools combine various applications, including data warehousing, discovery, and visualization.

A diagram illustrating a data warehouse used for Life Cycle Assessment (LCA) reporting. Raw data from multiple systems is collected in the data warehouse, where it is processed and harmonized into a dataset. The diagram shows how data flows from sources such as warehouses, factories, and transportation systems, supporting business intelligence and decision-making.
Data Warehouse combining data from multiple sources — (Image by Author)

BI solutions interact with these systems to:

  • Process and translate the acquired data into a single harmonized source
  • Build user-friendly reports, charts and maps

💡 For more details,

After reporting, what’s the next step?

Your company may use the results and insights to implement a sustainability roadmap with initiatives.

In the following section, we will see how data can be used to simulate the impact of these initiatives.

Next Steps

This article mainly focused on how data analytics can support data gathering, processing and visualization with diagnostic tools.

What if we want to estimate the impact of reengineering solutions?

Simulate different scenarios with a Digital Twin

A digital twin is a digital replica of a physical object or system.

This model represents your warehouses, transportation networks, and production facilities.

Example of Digital Twin with Python — https://samirsaci.com
Example of Digital Twin with Python — (Image by Author)

In this digital world, you can model each element of your end-to-end supply chain with costs, energy, emissions and lead time parameters.

What if we deliver European market from a warehouse in UK?

When brainstorming potential decarbonisation strategies, you can simulate their impacts on the supply chain.

Example of several scenarios simulated using your digital twin — https://samirsaci.com
Example of several scenarios simulated using your digital twin — (Image by Author)

For example,

  • What would be the impacts on logistic costs and CO2 emissions if we localize the production?
  • What would impact your inventory and store delivery lead time if we use alternative (green) cultivation methods outside India?
  • What would be the costs of implementing a logistic network to collect used items at stores?

For more details on how to implement this solution,

Have you heard about Supply Chain Optimization?

Sustainable Supply Chain Optimization App

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

A flowchart illustrating the steps for sustainable supply chain optimization. It shows three stages: importing data from three factories, selecting an objective (such as minimizing waste, cost, or CO2 emissions), and visualizing the results through maps and charts. Each factory has different goals, such as reducing waste, lowering costs, or minimizing carbon emissions.
Workflow of the Supply Chain Sustainability App — (Image by Author)

What is the optimal set of factories to minimize CO2 emissions?

Suppose you want to design a supply chain network to produce and deliver products to specific markets.

You have

  • The demand per market in (Unit/Month)
  • A set of potential factories with their fixed/variable costs, environmental impact and locations
  • Transportation costs and environmental impacts from the factories to each market

What would the cost impact be if we want to minimize CO2 emissions?

A set of three world maps comparing different supply chain optimization scenarios. The first map shows the initial solution, the second adds energy and water usage constraints, and the third adds CO2 emissions constraints. Below each map, a Sankey diagram visualizes the cost impact, with the final scenario showing an 18% increase in costs due to the additional constraints.
Simulation of multiple scenarios — (Image by Author)

With this solution, you can simulate several scenarios, playing with the objectives (e.g., minimizing CO2, cost, or water usage) to get the optimal network.

I developed a web application that simulates several scenarios of green transformation to estimate the impact on the overall cost of production.

For more details,👇

What about unstructured data?

Automate Accounting Tasks using Python

As explained in the previous section, we may need to extract information from unstructured data like Excel files.

For instance, your utility bills may be in Excel files.

How can we automatically extract and process information from these Excel files?

In another article, I share my method for automatically extracting and processing data from an Excel file using Python.

A flowchart titled “Design Automation Tools for Excel Users” showing the steps for creating Python-based automation tools for sales analytics. The steps include: 1) Data analysis to provide insights, 2) Automate the calculation using Python, 3) Export the Python code into an executable (.exe) file, and 4) Share the file with colleagues. The diagram emphasizes simplifying Python-based automation for non-technical Excel users.
Data extraction and processing workflow — (Image by Author)

The approach is simple: your Python script will automatically open files (that respect a specific template) to extract and process data used to build a report.

A detailed Excel table showing monthly operation costs, including items like equipment rentals, investments, and purchasing. The table lists various types of equipment (e.g., reach trucks, laptops, laser printers) along with associated costs for renting, purchasing, and investment, broken down by categories like unit cost, depreciation, and VAT. This table serves as an example of unstructured data that can be processed using Python automation.
Example of Template of Input File — (Image by Author)

Like the example above, these files can be your utility bills or supplier invoices.

The output can be used to generate reports or feed an SQL database.

For more information about this methodology, check the article linked below. 👇

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.

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References

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Published in Towards Data Science

Your home for data science and AI. The world’s leading publication for data science, data analytics, data engineering, machine learning, and artificial intelligence professionals.

Written by Samir Saci

Top Supply Chain Analytics Writer — Follow my journey using Data Science for Supply Chain Sustainability 🌳 and Productivity ⌛ https://samirsaci.com/about

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