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Four ways data science can simultaneously deliver environmental & commercial quick wins for the fashion industry

Four ways data science can simultaneously deliver environmental & commercial quick wins for the fashion industry

A sustainable mindset has become a pre-requisite rather than a differentiator in the fashion industry. Whilst companies work towards a longer-term materials revolution, there are quick wins to be had by leveraging the power of data across a wider scope of business operations.

Author : Sona Abaryan & Mathilde Hellier

Date : Jan 14th 2021

Category : Thought Leadership

In today’s world, it pays to be green. A recent study conducted by Nielsen revealed that 73% of consumers are willing to modify their consumption habits to limit the impact they have on the environment; and in this context, 46% of that same population say they would forgo a brand and switch to an eco-friendlier alternative. Whilst a large part of the movement towards sustainability lies in renewable and ‘clean’ resources, improvements in the usage of data- and AI-empowered business processes can also have an impact in reducing our environmental footprint.

In the past few years, the UN Conference on Trade and Development listed the fashion industry as the second-largest source of pollution in the world; with more than 70% of clothing ending up in the landfill, the environmental impact is estimated to be just behind that of the oil production industry. In response to this kind of observations, fashion retailers are increasingly acknowledging their share of the responsibility and actively promoting eco-friendly initiatives, often launched by freshly hired ‘Chief Sustainability Officers’.

These conversations around sustainability led to industry leaders signing the G7 Fashion Pact in 2019 to formalise their promises and translate talk into action: 

  • H&M has committed to using 100% sustainable materials by 2030 after launching its Conscious collection of eco-friendly fashion pieces in 2012
  • Zara intends to fulfill the same promise by 2025

With a new generation of customers that are more purpose-driven and increasingly favourable towards brands that champion ethical and eco-friendly values, companies that are quick to clean up their act will be rewarded on the short and longer term.

How Data Science can help

The solution to sustainable fashion is as multi-faceted as its definition. BoF recently launched its Sustainability Index, bringing together a diverse group of fashion sustainability & worker’s rights experts to track the fashion industry’s progress towards urgent environmental & social transformation across 6 key dimensions. 

We can all agree that there is no one single solution to all topics and to that end, this white paper focuses predominantly on tackling waste & emissions with the help of data.

With a heightened focus on sustainability and the risks/rewards highlighted, consumer brands are increasingly looking for ways to rethink their business models and product portfolios to hit commercial and sustainability targets. The solutions vary by industry and commonly discussed solutions are certainly to adopt eco-friendlier materials and production methods, limit the use of water or toxic chemicals, rethink packaging, and engage in recycling activities. But an area that gets less public attention is around the end-to-end supply chain efficiency level with the aim to limit wastage, unnecessary transportation, and superfluous production. This is where data science can help.

In this article, we explore a holistic approach that brands can use to rethink their end-to-end supply chain and ultimately limit their environmental footprint from production planning all the way to the customer’s door.

1. Predict the right level of demand for production to avoid inefficiencies & resource waste

Getting a sense of customers’ needs and wants through data is crucial; it can help businesses steer their planning proactively, manage inventories effectively, and meet demand more accurately. Brands that understand demand well can limit production and distribution inefficiencies which often bear tremendous financial and environmental costs.

Zara, well-known for its disruptive distribution network, commits only 50% of inventory when entering a new season, leaving the remaining half agile to respond to customer trends of the time. Furthermore, the smart chip technologies integrated in the brand’s clothing act as real-time data hubs, allowing the retailer to regulate its in-store replenishment process but more importantly, support its ‘just-in-time’ production. Although this approach is widely praised, Zara is relatively alone in this space. Whilst the latter waits for sales data to inform production, Rue21 takes a more proactive approach by engaging the customer in the decision process through surveys & questionnaires before any production begins. This allows the teen fashion brand to have a better sense of which products customers want in order to allocate production resources more efficiently and bring to market promising products only. The common thread between the two brands the willingness to understand demand and tying it to the production & stocking efficiency that benefits the bottom line and reduces environmental impact.

But despite major leaps in the field of data science, the ways of forecasting product demand in many large corporations have not changed a great deal over the past 20 years and are based on either expert-driven or tool-driven fundamentals. While the expert-driven approach struggles with consistency and operational efficiency, the tool-driven approach suffers from a lack of adaptability, interpretability, and a scope limited to existing products. Fully leveraging the power of AI to take product-level demand sensing to another level is a tough challenge, but the business & environmental value potential is immense. 

We believe the answer lies in a flexible approach that encompasses both trend detection for product development, as well as demand forecasting for production that factors in external data sources for early signals, and allows for human input. The success of this process has been proven time and time again with our clients. 

For more details on how to successfully build a demand sensing capability, check out these article below:

2. Limit distribution footprint through smarter assortment

Assuming overall demand has been accurately assessed, the next step is to create the most suitable product assortment per point of sale to avoid replenishment issues and unnecessary product travels; translating into economies of scale on distribution costs and a reduction of harmful emissions. But whilst many companies define assortment according to the point of sale’s qualitative demographic and socio-economics surroundings, or simply by store size & type, only a few companies leverage the power of AI to optimise the product offering through the consumer lens. Our clients often look to us to help them adapt assortment according to purchasing patterns observed at a store/account and geographical level. This will become that much more important in the wake of the pandemic as the lack of tourism will result in fundamental shifts in the make-up of the customer in each location. Moreover, for retailers that are able to leverage their stores for e-commerce delivery, it becomes crucial to get assortment & stock supply right to meet the omni-channel demand locally. 

Beyond profiling consumers on purchasing behaviours, understanding product affinities can also help further enhance product portfolio decisions. By applying machine learning to the data behind customer receipts, companies can better identify complementary products which could be promoted & displayed together, as well as substitute products that cannibalise each other and should not form part of the same store assortment. Executing against these findings will increase average order value and enhance sell-through at a store level. Moreover, all this data can be fed back through the value chain to fuel demand sensing algorithms, fostering efficiencies across the different production & distribution phases and proving the impact data science can have on boosting sustainability.


3. Rethink promotion strategy to prioritise value over volume

Decades of price slashing and discounting has created a culture of disposability in the fast fashion industry. But the race is now back on to put the value at the centre of the purchasing decision. As customers emerge out of the COVID-19 crisis, they are likely to be more conscious of consumerism and their discretionary spending. It is, therefore, a pivotal moment for brands to build back value into their image and products. 

Even before the COVID-19 crisis, many brands were still falling into the trap of all-year-round discounts cutting into margins without compensation in volumes. Higher discounts, on the other hand, yield a bigger volume response but are not sustainable. Outside specific promotional events, such as Black Friday or clearance, where large incremental sales are gained and brands cannot afford not to participate, the volume increase from small to medium discounts rarely compensate for the revenue losses. One thing is certain – there is almost always a trade-off to be made between revenue, volume & the bottom line when it comes to promotions. 

In order to optimize revenue & profits, fashion brands need to understand the customers’ willingness to pay (elasticities) at a product level, allowing them to differentiate discounts to yield the optimal response against their objective. Working with a fashion retailer, we also observed the halo a key promotional period can have on full-priced items by driving footfall, stressing the importance of setting up the right promotional strategy in terms of product that is designed to maximize revenues without the need to slash prices across the board.

Of course, the fashion industry has always had a need to clear stock before the next season, however, with the advances in demand prediction and a fresh approach to the frequency of seasonal product drops as a result of the shake-up during COVID-19, the sheer volume of the challenge should reduce.


4. Empower decision makers by quantifying the environmental impact

If there is any hope of making a concrete difference, decision makers need to be empowered to make better choices: customers & brands alike. This is where quantifying the impact plays a key role. 

In a recent report, Veja showcased the different scopes of study used when considering the CO2 footprint. And while most of the companies consider (and often reveal) solely the emissions linked to their direct activity (production and distribution of sneakers in Veja’s case), many ignore the emissions coming from the activities carried out by employees, suppliers the partners. It includes: the employees’ commute, the business travels, the warehouses electricity consumption etc. Knowing how to quantify the full impact of a business can be an enabler for internal decision markers to rethink some activities (eg: opening a new distribution center, change the office location…) and serve both economical and environmental objectives simultaneously.

As a customer, the switch to e-commerce through the pandemic has been a natural & convenient transition. However, the e-commerce industry appears to be a carbon-intensive alternative to traditional brick and mortar retail, especially for large items. 

  • First because online deliveries require additional packaging
  • Second because customers tend to make smaller but more frequent purchases which increase the number of micro-deliveries
  • Third because items often come from different distribution centres which augments the number of dedicated shipments
  • And fourth, because most of the time customers are likely to return their purchase, due to the little effort it represents, which leads to extra-large orders and thus bulkier deliveries on the way out and additional delivery on the way back

While technologically advanced gadgets (using AR / VR) can tackle that last point by offering the opportunity to customers to ‘try on’ clothing and be more conscious of their purchase, fashion retailers can look at some ‘quicker’ wins.

Taking inspiration from brands like Farfetch with its recent tool that helps online customers to choose items with lower environmental impacts (including secondhand clothing and those made from renewable materials), brands can educate their clientele by measuring the environmental footprint of various means of receiving an order (based on the size of the parcel, the delivery location, its proximity to stores/collection points, etc.). 


Environmental & commercial wins in one

Gradually, a sustainable mindset has become a pre-requisite rather than a differentiator in the fashion industry. Whilst companies work towards a longer-term material revolution, there are quick wins to be had by leveraging the power of data. Market players need to consider a wider scope of business operations in establishing their sustainability agenda – from using market signals for demand sensing, all the way to empowering the consumer to make more conscious choices. The correct exploitation of the data stemming from this extended supply chain can lead to an enhanced and more competitive business model by tying economic and environmental successes together. 

Ready to talk or simply curious to learn more? Reach out to our experts to understand how your operational efficiency challenges can be tackled through data science. Follow the links below :


The environmental pressures in the FMCG industry (2018)


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