Artificial intelligence and machine learning is reshaping the entire fashion value chain, from designing and manufacturing to marketing and sales. Industry leaders are leveraging AI technologies to gather data and improve overall performance from top to bottom for a more seamless customer experience.
The COVID-19 crisis has further highlighted the importance of artificial intelligence and machine learning in the apparel industry. Businesses early to integrate AI in their operations are more likely to emerge successful post-pandemic while the laggards are struggling to survive.
Artificial Intelligence in the Apparel Industry
The apparel industry is still in the early stages of integrating AI and ML in their processes, however, the results are promising. A study by McKinsey found that 75% of retailers in 2018 planned to invest in artificial intelligence to transform their entire business model. By 2022, the spending is predicted to reach $7.3 billion in the fashion and retail sectors.
In order to be a leader in the industry, brands need to invest in digital solutions to reinvent their supply chain. A study by Capgemini Research Institute discovered that 41% of leading global retailers planned to integrate artificial intelligence to their businesses while 28% had already done so.
Businesses that don’t adopt can no longer be sustainable and risk being surpassed by other brands in today’s hyper-competitive environment. It was discovered that 44% of retailers in the fashion industry that failed to leverage artificial intelligence faced major financial losses. The pandemic has only further accelerated the need for deep learning technologies and predictive analytics to increase efficiency and productivity across the value chain and meet consumer demands. A report by IBM found that the fashion sector would leverage predictive analytics primarily for supply chain planning, demand-forecasting, and customer intelligence.
Top fashion brands such as H&M have already implemented AI in various areas of their business to help them achieve their sustainability goals. The company started their artificial intelligence department in 2018 in an effort to be more data-driven, decrease waste through demand-forecasting, and ensure that the ‘right products are in the right place at the right time.’ Other leading brands such as Nike have invested in AI to minimize returns and ensure that their customers receive the best quality product.
AI can significantly improve the textile manufacturing process and enable brands to minimize errors, enhance color match, and streamline quality assurance processes. Digital solutions leveraging advanced artificial intelligence can help manufacturers identify faults inline which in turn can minimize losses and decrease overall waste production.
Artificial intelligence and machine learning can also enhance quality checks by reducing human error, accurately matching colors within tolerance, and ensuring that the best quality fabric is selected during fabric grading. Studies found that quality checks backed by artificial intelligence technology can reduce the chances of low-quality fabric by up to 90%.
Machines powered by automation can increase savings and enable faster delivery while maintaining premium quality. A study by Capgemini Research Institute predicted that AI could save the fashion industry up to $340 billion in the next two years.
AI also enables brands to better determine demand and can decrease forecasting error by up to 50%. Predictive analytics help brands manage inventory, avoid overproduction, and minimize losses. The fashion industry is one of the biggest producers of waste due to overproduction and digital solutions powered by smart predictions can help the industry reduce inventory by 20% to 50% through more accurate predictions. Additionally, machine learning can improve logistics by providing alternative routes, decrease shipping costs, and reduce transit time.
The application of deep learning technologies in the supply chain can increase efficiency, transparency, and improve overall performance. With better data, companies are able to make more informed decision and determine demand.
The use of predictive analytics can help brands increase sales and offer a more seamless customer experience. Virtual assistants such as chatbots allow brands to gather information and offer a more personalized experience for the customer. Fashion brand ASOS was able to increase sales by 300% by integrating chatbots on their site. Top brands such as Levi’s also leverage chatbots to help their customers find the best pair of jeans. It is predicted that artificial intelligence will manage up to 85% of all B2C interactions by 2020.
A study by Body Labs found that $64.2 billion worth of apparel and footwear is returned due to sizing errors. AI-powered solutions such as fit analytics, used by big brands such as North Face and Tommy Hilfiger, that recommend sizes based on customer information such as size, height and weight can help reduce this by recommending correct sizes.
Retailers, with the use of artificial intelligence technology, can get a better understanding of customer behavior and modify their processes to better suit their needs in the future. In store analytics offer a deeper understanding of the customer and enable brands to offer a more customized experience.
Artificial intelligence can also help brands with sustainability initiatives, which is an important factor for consumers today. Machine learning can help businesses identify the most sustainable suppliers and methods of transport. With more accurate information on demand and increased visibility into the supply-chain, artificial intelligence can contribute to sustainability in the fashion industry.
The COVID-19 pandemic has increased discussions around artificial intelligence and machine learning as consumers become more aware and shift towards sustainability. Businesses that have adopted are able to make better, more informed decisions and increase revenue.
AI is essential for the survival of businesses today. Successful brands will adopt artificial intelligence throughout their entire business model to increase efficiency, productivity, and optimize performance while laggards become irrelevant.
Data is the foundation of artificial intelligence and machine learning algorithms. However, the first step is correct data collection at the right time, which helps in prescriptive analytics. Artificial intelligence comes after, as it’s all about decision automation to generate business value.
Manual data collection can lead to increased errors and inaccuracies. Our digital solutions enable businesses to make better, more informed decisions and increase overall efficiency across the entire supply-chain through accurate data collection in real-time and making use of that data for decision automation by employing machine learning algorithms.