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In this installment of the AI in Supply Chain series (#AIinSupplyChain), we explore how Chain of Demand, an early-stage startup based in Hong Kong, is helping companies in the retail industry apply AI and machine learning to increase their profitability and sustainability.
Chain of Demand — born out of insights gained in the retail industry
I spoke with AJ Mak, founder and CEO of Chain of Demand. As is customary with these #AIinSupplyChain articles, my first question for him was, “What is the problem that Chain of Demand solves for its customers? Who is the typical customer?”
He said: “Our goal is to improve profitability and sustainability for the retail and supply chain industries. By using our AI analytics, we help retailers to optimize their inventory, which improves margins by minimizing their inventory risk, markdowns and excess inventory. Reducing excess inventory is a huge factor in reducing carbon emissions and water wastage, and this is now more important than ever.”
He added, “Our typical customers would be omnichannel retailers and brands in the apparel, footwear and beauty and cosmetics categories.”
Chain of Demand’s secret sauce
Next I asked, “What is the secret sauce that makes Chain of Demand successful? What is unique about your approach? Deep learning seems to be all the rage these days. Does Pathmind use a form of deep learning? Reinforcement learning? Supervised learning? Unsupervised learning? Federated learning?”
“Our secret sauce includes our veteran experience and domain expertise in retail, and predictive models tailored for the industry,” Mak said. “We use deep learning for our image recognition and modeling, which includes supervised learning, unsupervised learning and reinforcement learning.”
Data is consistently an issue. I asked, “How do you handle the lack of high-quality data for AI and machine learning applied to legacy industries?”
“Part of our AI is used to extract, transform and load ‘dirty data’ from legacy systems,” Mak said. “We have done a lot of data cleaning from many different legacy systems, and we have been able to streamline the ETL (extract, transform and load) process for the retail industry.”
Bluebell Taiwan — a customer case
In a case study published on its website, Chain of Demand describes how it helps its customers.
Bluebell Group helps luxury brands establish a presence in Asia through a platform consisting of 600 online and brick-and-mortar stores spread over more than 10 countries in the region.
Due to changes in the behavior of shoppers, Bluebell needed to help Jimmy Choo Taiwan reconcile how much revenue would be generated by in-store sales in comparison to online purchases. Using Chain of Demand to test and incorporate AI during the merchandise planning process, Bluebell achieved a 90% improvement in the accuracy of its predictions of best- and worst-selling items. Bluebell also increased its accuracy predicting the number of units sold by 81%.
Industry experience is an advantage
In my conversation with Mak, he pointed out that one reason he believes Chain of Demand fares well against the alternatives is that his family has operated in the apparel and fashion retail supply chain management business since 1981. He spent nearly a decade in the business, gaining an understanding of the problems in global apparel and fashion retail supply chains. That experience and those insights inform how Chain of Demand goes about building its product.
When I asked him about competitors, he mentioned Blue Yonder and Celect.
Coincidentally, José P. Chan, who was then the vice president of business development for Celect, was a speaker at #TNYSCM04 – Artificial Intelligence & Supply Chains, organized by The New York Supply Chain Meetup in March 2018.
Celect was purchased by Nike in August 2019 for a reported price of $110 million.
Companies like Chain of Demand want to get large companies away from using spreadsheets for sales forecasting and demand planning. As it becomes necessary to take an increasing number of sources and types of data into account, the case for shifting away from simple spreadsheets and onto more robust and sophisticated platforms will only gain strength.
That must sound like music to Mak’s ears.
Conclusion
If you are a team working on innovations that you believe have the potential to significantly refashion global supply chains, we’d love to tell your story in FreightWaves. I am easy to reach on LinkedIn and Twitter. Alternatively, you can reach out to any member of the editorial team at FreightWaves at media@freightwaves.com.
Dig deeper into the #AIinSupplyChain Series with FreightWaves.
● Commentary: Optimal Dynamics – the decision layer of logistics? (July 7)
● Commentary: Combine optimization, machine learning and simulation to move freight (July 17)
● Commentary: SmartHop brings AI to owner-operators and brokers (July 22)
● Commentary: Optimizing a truck fleet using artificial intelligence (July 28)
● Commentary: FleetOps tries to solve data fragmentation issues in trucking (Aug. 5)
● Commentary: Bulgaria’s Transmetrics uses augmented intelligence to help customers (Aug. 11)
● Commentary: Applying AI to decision-making in shipping and commodities markets (Aug. 27)
● Commentary: The enabling technologies for the factories of the future (Sept. 3)
● Commentary: The enabling technologies for the networks of the future (Sept. 10)
● Commentary: Understanding the data issues that slow adoption of industrial AI (Sept. 16)
● Commentary: How AI and machine learning improve supply chain visibility, shipping insurance (Sept. 24)
● Commentary: Can AI and machine learning improve the economy? (Oct. 8)
● Commentary: Savitude and StyleSage leverage AI, machine learning in fashion retail (Oct. 15)
● Commentary: How Japan’s ABEJA helps large companies operationalize AI, machine learning (Oct. 26)
● Commentary: Pathmind applies AI, machine learning to industrial operations (Nov. 20)
Author’s disclosure: I am not an investor in any early stage startups mentioned in this article, either personally or through REFASHIOND Ventures. I have no other financial relationship with any entities mentioned in this article.
