Artificial Intelligence for Inventory Management.

Inventory management is best orchestrated by a small, tight knit crew.
Amazon has applied A.I to inventory management at an unprecedented level
  1. Demand Prediction for Inventory Management. This is the simplest approach and, if implemented correctly, can be very informative. As the name suggest, the general idea is to build a time series prediction model that can estimate what demand will be like for the coming days across all items in your inventory. If your company has its own dev team that are familiar with machine learning, we’ve found that some of the highest performing time series methods are currently lstm/rnn models with sliding windows, old school logistic regression with a few tweaks and finally certain probability models, One of the joys of demand prediction is that you can incorporate external data sources into the system to see if they have an impact on demand. We have built systems that ingest the weather data to see if that impacts on prediction — for example we recently undertook a proof of concept for a Fortune 100 company that uncovered a strong causality between temperature above 30 degrees Celsius and part failure. This was done by simply feeding weather data and their inventory data into a single artificial intelligence model and the AI did the rest.
  2. Reinforcement Learning systems for full-inventory management. This is the more advanced artificial intelligence approach that involves a model taking serious control of the inventory operations, with human checks and balances. Reinforcement Learning is a domain in artificial intelligence where the models don’t simply make predictions or classifications, but actually act on these predictions. It’s about giving an artificial intelligence the option to act on what it’s predicting. This is done by rewarding and punishing the model for acting incorrectly. In this case, we typically establish punishments for letting an particular inventory item run out of stock, we also punish the model for stock too higher value for too long. For rewards, we primarily focus on ordering items within a safe window before the demand. Reinforcement Systems are hard to implement oneself without prior experience — you need familiarity with simulation models and RL to get anywhere with Inventory. But when done right, they yield phenomenal results. One of our implementations saw a 32% reduction in costs across the operation.

The official blog of Remi A.I, an Artificial Intelligence studio with offices in Sydney and San Francisco.

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Remi AI

Remi AI

The official blog of Remi A.I, an Artificial Intelligence studio with offices in Sydney and San Francisco.

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