
The conversations in the fashion industry are definitely changing. With more awareness of the harmful effects of fashion activities on the environment to other challenges in running a successful fashion business.
Some of these challenges revolve around the attempt to balance sustainability with profitability while maintaining a customer-centric business model.
Keep reading…
The term "sustainability" has become a buzzword in the industry with some brands truly knowing what it means yet have chosen to follow the "green-washing path". While others do not know what it entails, assuming it’s majorly about using cotton and hemp, forgetting to consider the amount of water wasted, excessive production, fumes generated, poor labor wages and working conditions, etc. Check here for some stats on water challenges due to increasing cotton consumption
A lot of light has been shed on "sustainable fashion" so I would like to tell you more about how Data Science is helping fashion companies build a more sustainable business.
Let’s go!!!
Data Science is a field of study that applies multiple disciplines such as ;
- Machine Learning Algorithms
- Business Intelligence
- Exploratory Data Analysis
- Data Product Engineering, etc
in order to extract actionable insights from raw data. It involves data cleaning, analysis, processing, advanced analytics, and the final presentation of insights and observations/patterns to stakeholders in order to make data-driven decisions.
Some skills required in Data Science include;
- Software Programming
- Mathematics
- Statistics
- Critical Thinking, etc.
Now to the question…
Can Data Science help the fashion industry be more sustainable?
The answer is YES!

Gone are those days when Fashion companies will only make design and sales decisions from unstructured data without listening to what the data is saying about customer preferences and journeys.
With the adoption of Data Science, some fashion businesses have been able to make more profit while attaining sustainability.
4 Unique Ways Data Science Is Making The Fashion Industry More Sustainable
1. Generate insights from social media and other online channels: Heuritech for instance leverages social media data to derive specific clothing details for a comprehensive understanding of fashion products and markets.
Heuritech scrapes millions of public social media contents, apply computer vision Technology, machine learning forecast algorithms, and market intelligence platform to accurately predict fashion trends. (Source: Heuritech)
Thus reducing the waste that would have been generated due to excess production or poor distribution.
This, in turn, helps fashion businesses be more profitable and sustainable.
2. Predict The Right Amount Of Products To Produce: It is not enough to accurately predict the upcoming trends, knowing the right demand for each product for the different seasons will ensure effective inventory management, mitigate production and distribution inefficiencies, and help business owners make a more informed and data-driven decision.

Zara has successfully integrated different digital strategies and technologies to improve sales and the accuracy of demand.
For example,
The integration of smart chip technology in the brand’s clothing generates real-time data which can be used to identify what fashion items the consumers are coming in contact with the most.
Check the article below for a deep look into how Zara leverages Big Data in supply chain management.
Some fashion brands engage directly with the customers via surveys and questionnaires prior to a new production season.
This approach to production helps to control inventories, reduce production capital, waste production, thus fewer chances of clothes ending up in the landfill.
3. Supply Chain Optimization: Perhaps the design and sales forecast has been very accurate, there is still a need to create to correctly recommend products and distribute at the right time to the right store.
What’s the point of a good trend forecast without a proper distribution?
That’s where AI technology comes in. This approach is used to demystify the purchasing patterns observed in fashion store records/ data or a buyer’s account on the e-commerce store.
Thus, ensuring the right product assortment, supply, and recommendations to the appropriate customers or stores at the right time.
Interestingly,
The data generated during this phase can also be fed back into the system to fuel demand-sensing algorithms and integrating data across the different stages of the value chain.
4. Effective Promotion Strategy: Take, for instance, a certain retail brand ABC, which decides to run a sales Ad for a particular set of items. And then was able to sell off all!!!.
Sounds great right?

But can we really say the ABC brand made the right decision to put up the sales ads?
What if the retailer could predict how the ads would run, perhaps more clothes could have been produced earlier.
or maybe the discount wouldn’t have been necessary. Thus resulting in more brand awareness, more sales, and more profitability.
Sounds more sustainable right ?
I agree!
Gone are the days when fashion brands had no choice but to run sales at different times of the year without necessarily knowing what the outcome would be. With the right AI technology, fashion brands can optimize product recommendations, understand consumer behaviors and thus know how to retain and retarget the right audience.
It’s no longer news that the fashion industry is impactful, we just need to decipher how to make it positive.
According to some schools of thought, the fashion industry is one of the largest contributors to global warming, reportedly being responsible for around 20% of the world’s industrial water pollution. (Source: TechRepublic)
Some other reports say this statement is backed by minimal evidence and there are a lot of inaccuracies regarding data and statistics on fashion waste.
On the other hand, if there is any hope in making the industry more sustainable with a more positive impact on the environment, then there is a need for the fashion industry to quantify these impacts.

Whether it is the amount of waste generated, or the fumes produced from machines, or exhausts from the vehicles (logistics). Maybe it’s the noise pollution and the amount of carbon released from the use of generators in fashion companies.
All of these need to be studied in order to accurately understand the impact of the fashion industry as well as how Data Science is making a difference.
Here are some facts on fashion and some of its unsustainable practices.
In conclusion, as a fashion brand or retailer, here are 10 questions Data Science can help you answer:
- Who are my customers?
- Where are they?
- What products do they want?
- How do I reach out to them?
- How much should I produce?
- When should I re-stock?
- Who and when should I offer a discount?
- When will they likely buy?
- Where else do they shop?
- Who are my competitors?
In as much as this article’s focus is the fashion industry, the technologies discussed also apply to other industries and businesses.
I hope you enjoyed reading it?
Do you also think Data Science can make a business more sustainable?
Comment in the chat section.
Re-Imagine The Business Of Fashion–part 1 ( How Can Fashion Brands And Retailers Re-Imagine Fashion Through Advanced Analytics? )
Re-Imagine The Business Of Fashion -part 2 ( Data Science can make the fashion industry become more sustainable. True or False?)