Can AI fix the retail supply chain?


by R Tresdahl and Dag Lioden

With the rapid adoption of generative AI technology like ChatGPT, there is no doubt that we are at a pivotal moment in history. In the coming months and years, thousands of companies will build on top of larger language models and explore their many applications. One promising application is using GPT agents to analyze data, which has the potential to revolutionize the global retail supply chain.

Why? In short, data is everything in retail. Information about what consumers want, how much product is available, and what is being shipped is critical to helping retailers and suppliers meet consumer demand and increase profits. But it has sometimes been challenging to obtain data about the supply chain, let alone gain meaningful insights.

Thankfully, that is starting to change. And with the right data input, GPT-powered AI has the potential to transform the retail industry, leading to stocked shelves, happier consumers, less waste and higher profits.

Enabling Data in the Supply Chain

There are endless opportunities to implement a large language model like GPT-4 in retail, but three main categories come to mind:

democratization of dataData is a powerful tool in CPG, but it is often silenced in organizations. GPT models can change that by allowing non-technical users to analyze data to answer key business questions, relying on natural language instead of SQL or data scientists. For example, a conversation with a GPT analytics bot might start with a question like “What was the average sales velocity for my peanut butter product in Chicago last year?” The real value of AI-backed analytics comes when the user asks, “Why is this down?” The bot performs root cause analysis on the fly.

active surveillanceData can be the answer to important business questions, but what if you don’t know which question to ask? AI can proactively monitor sales and inventory data throughout the supply chain and alert retailers and suppliers to critical events and anomalies if sales drop more than 10% at a certain retailer. While machine learning can do this today, it is labor-intensive to set up and requires ML engineers to train the model you are looking for. With big language models, AI can learn On his own What’s normal and what’s anomalous, including patterns we haven’t thought about yet. For example, during the early stages of a pandemic, AI could catch changing consumer behavior and recommend increased production of toilet paper or pantry staples before encountering empty shelves.

autonomic function: The next step that AI can take is not only to detect a pattern and make recommendations, but to automatically take action based on its findings. There are countless events happening every day in thousands of retail stores, warehouses, or manufacturing facilities, and humans in retail today must stick to priorities they can realistically implement on their own. But with AI, they can monitor any of these events and take action, adding up to real wins. For example, if the AI ​​detects rising temperatures during a heat wave, it can automatically allocate excess ice cream inventory toward the hottest stores.

Real-world applications already underway

Brands and retailers are already using ChatGPT to better serve customers and streamline operations AI chatbots In customer service, for example. Further advances in AI could make these interactions more personalized and valuable. Retailers and brands alike strive to build relationships with consumers through personalized recommendations, from product suggestions to recipe ideas. With the vast amounts of customer data retailers collect through their loyalty programs, the ability to process and learn from that data will be a valuable tool for marketers.

On the operational side, Walmart is already using AI To negotiate with suppliers on the cost of bulk items based on supply chain data. Another opportunity we are already taking advantage of at Crisp is to optimize supply chain management by detecting retail voids: situations where a product should be sold at a given store but is out-of-stock , is not caused by wrong location or damaged goods, or others. Mistake. AI models can learn general sales patterns at a store, identify an anomaly, spot a void, and alert the supplier or retailer – keeping shelves stocked and products at their most productive. Can solve one of the big pain points.

AI can also be used to learn from ingredient lists or consumer preferences and suggest new products. Tech-savvy brand Noteco has an AI-powered product development engine, Giuseppe, which can rapidly develop plant-based analog counterparts to traditional dairy and meat products. Giuseppe collects data on plant molecules that are found in dairy – including cabbage and pineapple – then puts them together to give a product that is remarkably similar in taste and texture to cow’s milk.

concerns and warnings

With all the potential surrounding AI, there are serious considerations that must be taken into account before its full potential can be realized in the supply chain, including:

  • Start with good data: Retail data needs to be clean, structured and accurate for AI to provide valuable analysis. retail data platform And ETL solutions can help by feeding clean data from retailers, distributors, e-commerce sites into AI-enabled tools.
  • context is key: Current GPT models can only take a limited amount of references, and brands with dozens of products in thousands of retail stores today can easily exceed that limit. Retail will need a GPT model that can process a high volume of inputs.
  • consider ethics: Ethical concerns abound with AI, and supply chain is no different – ​​especially if decisions are being made about how to allocate food. For example, in the case of food shortages related to pandemics, today we cannot rely on AI to decide how and where to allocate inventory without considering social good and public health.

While there is much more work to be done, we are already seeing the potential of AI to do what we alone cannot: create a supply chain that is more responsive, agile, profitable and sustainable in meeting the needs of consumers.

About the Author:

are terrible co-founder and CEO of crispAn open data platform that harmonizes and normalizes retail data to increase profitability and reduce waste.

Doug Leoden co-founder and chief product officer at crispAn open data platform that harmonizes and normalizes retail data to increase profitability and reduce waste.

The views and opinions expressed here are the views and opinions of the author and do not necessarily reflect those of Nasdaq, Inc.

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