How AI & ML are assisting fashion supply chain
The future would be about customers designing fashionable clothes with the help of AI, indicates Prakash Bajpai.
Personalisation is the key to enhanced customer experience. When it comes to fashion and apparel, the ability to customise garments based on customer preference will be the key to becoming a top fashion brand in the near future. Fashion brands would be able to give the choice of designing clothes to their customers. And while not every customer would be able to design clothes, what if they got a helping hand from AI?
Today we buy pre-designed clothes, available to use to choose from – whether it’s in a store or online. The designs are already there; we just pick the right size. We have no say in the fall of the fabric, the thread count, how loose or fitting it is. Technology can partner with consumers and assist them in understanding what they could be interested in.
The future would be about customers designing fashionable clothes with the help of AI. The AI tool would also collect a huge amount of useful data that machine learning algorithms can use to design future products and deliver personalised recommendations.
AI beyond designing clothes
The application of AI has already been recognised in the fashion and apparel industry at various stages such as apparel design, pattern making, forecasting sales production, supply chain management etc.
With the emergence of globalisation and digitalisation, AI has played a significant role in connecting businesses globally. In the last decade, the fashion and apparel industry has utilised AI to a certain extent for improving supply chain processes like apparel production, fabric inspection, distribution. This was important as the fashion and apparel industry is volatile and it is always challenging to quickly respond to change in trends and continuously evolving consumer’s demands.
AI & sustainability
AI’s deep learning and prediction algorithms now offer sustainable supply chain solutions. With the right data and technology, people and organisations involved in fashion and lifestyle retail can address difficult problems and transform the world. AI can be deployed to predict demand patterns and emerging fashion trends. Such data-driven production helps brands with inventory management and improving business impact decisions. Additionally, customer behaviour online can be used to create demand forecasts.
Supervise the machine learning algorithms, which can inform every action from designing garments to optimizing logistics. With further improvements in the AI support systems, fashion brands will have the scope to make more informed decisions about product development, sustainable production, and profit optimisation.
AI now also offers end-to-end fashion supply chain visibility to brands, which means brands can track every step of the production process, monitor where their raw materials are sourced from, and verify whether the factories are compliant and ethical or not. Such transparency and control over the production cycle empowers brands, who can build transparent relationships with their customers and answer all questions.
AI ethics & responsibility
We now live in a time when the power of AI is being understood and utilised by all industries worldwide. From strategic decision-making to marketing to something as complicated as supply chain management, AI has found utility everywhere. But an understanding of the power of AI should certainly be complemented with a responsible approach, now more than ever since certain factors such as privacy and algorithm biases have been identified. If not used responsibly, AI can be used in unethical ways that do not benefit the general public.
AI application development, along with Machine Learning (ML) provide an amazing opportunity for growth, change, and diversity, equity, and inclusion and technology companies should concentrate on the same to ensure AI systems respect human rights, diversity, and individual’s autonomy.
AI biases
AI biases could arise in any industry due to a few reasons. The primary ones are algorithms that are a result of the classification algorithm type selected, or when data is not large enough or representative enough of a problem. These impact the generalisation ability of an algorithm.
Fashion brands can try to ensure that such biases resulting from the algorithm can be reduced by making sure algorithm do not over fit (eg. an ensemble approach). Companies also prioritise the selection of representative data or consider a large dataset covering all possible scenarios. The results are monitored closely.
Businesses in the fashion and apparel space can deploy a number of ML models for various use cases. Fashion brands need to continuously develop new ones as well as modify and improve the existing ones. Depending on the nature of the problem, the models are re-trained at pre-defined frequencies.
To sum it up, do not go by the hype of data/ML/AI in choosing trend forecasting solution or another AI solution. Instead, look for the bias in the data and the algorithm. Make use of social signals only after sufficient validation. Teams need to evaluate AI for biases while reading their own data.
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