Can AI help shrink fast fashion’s carbon footprint? New research is promising
A new study demonstrates how AI can revolutionise fast fashion by improving supply chain efficiencies and in turn reducing the industry’s carbon footprint.
The fast fashion industry is valued at US$2.5 trillion and employs some 75 million people, however, its economic impact is underscored by its environmental pollution.
A team of international researchers has demonstrated how the fast fashion industry can better harness AI-driven technologies for climate action and reduce its 10 per cent contribution to the world’s carbon emissions.
Professor Shahriar Akter, the lead author of Unleashing the Power of Artificial Intelligence for Climate Action in Industrial Markets, was pleasantly surprised by the study’s findings.
“Everyone is interested to know how AI is contributing to organisational performance,” Professor Akter told Inside Retail.
“In our case, we have used AI [modelling] to show that AI can be used for implementing environmental performance of an organisation and at the same time can contribute to market performance.”
The capability requirements to innovate
According to Professor Akter’s research, ‘environmental performance’ refers to an organisation’s performance related to pollution control. Whether retailers are voluntarily or statutorily motivated to improve their environmental performance, AI-driven technology has the potential to reduce the manufacturing carbon footprint.
However, for retailers to develop AI-driven technology that can forecast and reduce their climate impact they need to already have an understanding of their environmental impact, be able to collect data in every step of the supply chain and know what consumers and competitors are doing in this space.
Akter’s study surveyed 211 managers at manufacturing companies in Bangladesh with at least one year of experience using basic AI-powered climate service solutions and found that businesses employing AI-powered climate service innovation models improved energy efficiency, reduced emissions, and increased renewable energy sources.
“We also found that focusing solely on environmental orientation is insufficient, and researchers must explore various technological and market dynamics to understand what constitutes more sustainable innovations in this area,” said Akter.
AI improves reputation and performance
The AI data-driven model developed by this research team, AIPCSI, has the power to generate and disseminate climate information and convert it into actionable forms to aid managers’ decision-making.
There is a reputational risk for retailers to not advance their affirmative climate action but there are also missed strategic opportunities.
According to Akter, AI has the potential to assist retail managers in crafting marketing strategies, enhancing brand image, accessing new markets, generating more profit, improving market share and gaining customer trust.
The study cited Finesse, an AI-driven and sustainable fashion label, as a prime example of a brand that utilises algorithms for demand forecasting based on its customer demographics, Gen Z, preferences.
Recently, both H&M and Tommy Hilfiger implemented AI-driven and predictive technology to understand the environmental nuances of each raw material and input in its supply chain.
AI technology, including AIPCSI, will allow retailers to have a proactive approach to their environmental performance rather than reactive.
The precedent for big fashion retailers to integrate AI-driven technologies into their environmental performance and market performance is already there.
In 2021, LVMH and Google announced their partnership to develop cloud-based AI solutions to enhance demand forecasting and inventory optimisation. Now, Inditex is planning to invest €2.7 billion into improving its online capabilities and technology upgrades.
“We have found that some literature says that if you go for environmental performance, you might compromise your market performance,” said Akter.
“We have carefully considered this hypothesis and we tried to basically prove the hypothesis for results that, no, it is not the case,” he concluded.
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