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Chain of Demand

How Artificial Intelligence is Fashion’s Perfect Fit

The following article is based around a talk spoken by AJ Mak, CEO/Founder of Chain of Demand, as featured on Weave Conference.


There is no denying that artificial intelligence is and will continue to disrupt every industry. Retail, however, is one area that can reap immediate benefits by leveraging this emerging technology.

 

Not only is the use of AI helping fashion retailers improve customization or customer communication, with predictive analytics solutions like Chain of Demand, brands can also reduce markdowns through better inventory management, faster product replenishment, and more accurate price optimization.  

 

Why AI for the fashion retail industry

According to Juniper Research, global spending by retailers for AI services is expected to reach $12 billion by 2023. One of the ways that AI and big data are changing the landscape of fashion retail as a whole, is through its ability to make faster, smarter business decisions. 

 

Rather than relying on traditional forecasting methods to make predictions that pull from a single data source, AI uses a range of sources, which in turn can save the industry as much as $340 billion, according to a Capgemini Research Institute Study.

 

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Retailers can use AI to scale across the value chain, from operations, supply chain, planning, product development, distribution, and more. According to an IBM report, it is believed that AI can help increase annual revenue growth by up to 10%. While boosting margins is important, Chain of Demand applies AI to the industry to improve profitability, as well as sustainability. 

 

Numbers don’t lie: AI is now

The industry realizes that using AI is critical for success. According to the Capgemini Research Institute, about 25% of the top 250 global retailers have planned to integrate AI into their organizations. 

 

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About 28% have already deployed AI in 2018 – a dramatic increase from two years ago in 2016, where it was just 4%. In addition, according to IBM, 85% of retail and 79% of consumer products companies will use AI for supply chain planning by 2021.

 

Examples of AI in fashion

Whether it is in retail operations, or product development, even big brands are acting quickly to apply AI in fashion. For example, Alibaba introduced their first ‘Fashion AI‘ concept store back in 2018, which helps automate the shopping experience. Built on RFID racks and AI mirrors, the brick-and-mortar store implements the elements of online shopping and takes convenience for consumers to a whole new level.

 

Big retailers like Tommy Hilfiger also in the game of AI in fashion, with the former teaming up with IBM and the Fashion Insitute of Technology (FIT) to create what is called ‘Reimagine Retail.’ This project uses AI to give retailers “an edge in terms of speed” and allows designers to create better, more customized pieces for their consumers.

 

“Companies that aren’t experimenting with this capability risk falling behind. They need to move quickly if they hope to remain competitive” (IBM Global Markets)

 

 

Deploying AI into fashion

In a 2019 report by IBM, they found that there were six main ways the retail industry planned on utilizing AI: supply chain planning (85%), demand forecasting (85%), customer intelligence (79%), marketing and advertising (75%), store operations (73%), pricing and promotion (73%).

 

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However, one of the biggest issues with the deployment of AI has not been about the technology itself, but with changing the mindset of management. 

 

Keys to successful deployment

Transition is never easy. When it comes to changing the process and traditional methods of doing things, resistance is to be expected. In fact, some of the major challenges that came from implementing AI into the organization came from the concerns of risks tied to the machine itself, complete integration, and adaptation. Whether it is having the right skills to execute or aligning the business strategy correctly, some of the biggest challenges came from a sense of unfamiliarity and lack of understanding for the technology as a whole.

 

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For AI to be deployed successfully, companies need to be informed on what skills are required for the right culture. Based on the IBM report mentioned above, the top three factors that influenced successful deployment in AI tended to be obtaining the right skills and resources, creating a culture open to change and adaptation, and aligning the strategy with execution plans.

 

When reflecting back on the numerous case studies that Chain of Demand have gone through, they were no different from the points mentioned above. Overall, when looking at the keys to successfully deploying AI, we found the following common factors across multiple brands:

 

  • Be industry-specific – The mistake that so many companies make when implementing AI into their organization is not being specific enough. Often times, they believe that AI is a be-all, end-all solution, and will magically fix their problems. This is far from being the case, as an effective solution needs to be industry-specific, with the right domain knowledge applied to the problem.
  • Start small – Oftentimes, many big brands which have been operating on traditional, conservative methods are unfamiliar with all this new technology that makes big, bold claims. For this reason, to earn the trust of those brands, it suggested that we start small
  • Prepare your dataData is the single most important factor in successfully unlocking true value in AI deployment, and making sure that the data is ready to be used. This is what will separate those who prosper in the future, from those who don’t.

 

The demand for change is now: Data is key

Adopting a data-driven approach is the way to go in this modern age. Companies like Chain of Demand harness AI and big data to make more accurate predictions. They help inform brands when to replenish their inventory stock, provide knowledge on the right amount to order, the right time to do so, and the right price to list it for. All of this ultimately helps to reduce markdowns, one of the biggest killers to profit today.

 

The problem with many older solutions in the market is that they are one-sided. There are limitations on how deep they can go, especially considering they mostly depend on one data source, as mentioned before. To make the most value of AI in fashion retail, retailers need to apply it throughout the whole value chain.

 

In the end, AI isn’t a magic formula that can eliminate every pain point at the snap of a finger. For it to be truly effective, retailers and vendors should begin entering as much data into their systems as early as possible, and start immediately. 

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