Good AI Vibes #2
Enhancing Online Order Holding, Unraveling Fashion Trends, and Overcoming Commodity Price Fluctuations
Hello everyone and welcome to the second issue of Good AI Vibes.
Get ready for some AI inspiration with Good AI Vibes. This newsletter is all about sharing ideas on how you can use artificial intelligence to level up your business and make data-driven decisions. Join us in transforming the way you do business through cutting-edge technology!
In every edition, you'll find 3 exciting and innovative uses of AI that are making a positive impact in various industries. Each use case will include a brief overview of the problem, how AI is being used to solve it, and the results achieved. In this issue of Good AI Vibes, we're highlighting the following:
The Art of Order Holding: A Win-Win for Online Retailers and Customers (E-Commerce / Operations and Logistics)
From Tweets to Sales: Unlocking the Power of AI and Social Media in Fashion Forecasting (Fashion / Marketing and Sales)
Outsmarting Price Uncertainty in Commodity Procurement (Cross-Industry / Procurement)
We hope that this issue of Good AI Vibes will inspire you to explore the possibilities and potential of AI in your own business, and perhaps even spark new ideas on how you can leverage AI to enhance your products or services. Stay tuned for more exciting AI use cases in our next issue of Good AI Vibes and subscribe to never miss an edition!
Also, please send us your feedback and suggestions to help us make this newsletter relevant and useful for you. You can email us at hello@goodaivibes.com.
Let's not wait any longer and dive into this issue's AI use cases that are making a real positive difference in various industries.
The Art of Order Holding: A Win-Win for Online Retailers and Customers
What's the business problem?
Imagine you're an online retailer juggling customer orders, trying to keep costs low and customers happy. How long should you hold onto a customer's orders before passing them off to a delivery partner for shipping? This is the order-holding problem, and it's all about striking the perfect balance between reducing order arrangement costs and avoiding delays in delivery.
What's the contribution of AI and how the solution methodology works?
AI steps in as a powerful tool to tackle the order-holding challenge. By utilizing a technique called Markov decision process, the AI system uncovers the ideal holding time for each customer. It takes into account factors such as order frequency, customer type, and demographics like age, gender, and city tier. By analyzing historical data, AI creates a personalized strategy for the optimal holding time threshold tailored to each customer.
What are the results?
Embracing AI-powered order management has proven to be a remarkable advantage for online retailers. By adopting the personalized threshold strategy developed by AI, retailers can outperform traditional benchmarks. This results in fewer order arrangements, shorter holding times, and increased customer satisfaction. Moreover, enterprise customers experience even more significant benefits from this personalized approach. With AI as an ally, online retail businesses can look forward to a more efficient and successful future.
From Tweets to Sales: Unlocking the Power of AI and Social Media in Fashion Forecasting
What's the business problem?
In the fast-paced world of fashion retail, predicting style color and jeans fit sales is a significant challenge. Retailers face hurdles such as short product lifetimes, long manufacturing lead times, and constant innovation in fashion products. Consequently, the ability to forecast demand accurately, especially when setting the initial shipment quantity for an item, is crucial for the success of fashion retailers.
What's the contribution of AI and how the solution methodology works?
AI comes to the rescue by leveraging social media data to forecast style color and jeans fit sales. By combining proprietary data from multinational retailers with publicly available data from Twitter and the Google Search Volume Index, AI models can develop accurate demand forecasts. These forecasts help retailers make better-informed decisions about the initial shipment quantities for their products. The AI model takes into account factors such as consumer preferences and market trends, helping retailers tap into the "bottom-up" changes in the fashion landscape.
What are the results?
Using AI and social media data to forecast fashion demand has proven to be highly valuable, with the out-of-sample mean absolute deviation improving by 24% to 57% over current practices. This approach has demonstrated consistent results across different retailers, markets, and geographic locations. With AI-driven fashion forecasting, retailers can respond more effectively to consumer preferences and market dynamics, which in turn drives better business performance.
Outsmarting Price Uncertainty in Commodity Procurement
What's the business problem?
In the world of commodity procurement, businesses face uncertainty when it comes to prices. With options to buy from forward and spot markets, businesses need to make smart purchasing decisions that minimize costs without compromising their needs. The challenge is to determine an optimal procurement strategy in the face of fluctuating prices and a plethora of available market data.
What's the contribution of AI and how the solution methodology works?
Enter AI, the secret weapon for smart procurement. Instead of relying on potentially flawed parametric price models, data-driven approach (DDA) leverages historical prices and real-time feature data like economic indicators to make informed decisions. The AI model uses mixed integer linear programming to set optimal purchase policies directly from data, focusing on the best possible decisions rather than just accurate predictions. By combining optimization with machine learning regularization, the model extracts valuable insights from the noisy world of data, all with the goal of cost minimization.
What are the results?
When it comes to commodity procurement, data-driven AI approach is a winner. By learning procurement policy parameters as functions of features, the model reveals the significant value of feature data. Although overfitting can be a challenge, machine learning extensions help improve out-of-sample generalization. The bottom line? DDA generates savings of an average of 4.3% over 10 years of backtesting compared to internal best-practice benchmarks. The best part? The AI model yields simple, optimally structured decision rules that are easy to interpret and put into action across various procurement settings.
We greatly appreciate you taking the time to read Good AI Vibes! We believe that these exemplary use cases have provided valuable and motivational insight for your quest towards achieving exceptional AI. We urge you to spread the word about our newsletter amongst like-minded friends and colleagues who share an interest in acquiring further knowledge on how to establish robust and efficient AI within their respective organizations.
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