Computers are very good at studying patterns and extracting insights from large amounts of data humans cannot easily comprehend. Discover how this works to your advantage as you aim to make the best Inventory Management decisions on Amazon.

Why is Demand Forecasting so Important?

When running an ecommerce or any retail business, after deciding what products you would like to stock for sale in what markets, the next most important decision to make would naturally be how much of it to stock and when exactly to stock up.

“What does consumer demand look like? Are buying patterns different in this marketplace compared to that? Is this product seasonal and hence, should I expect a lull or a spike in sales during certain periods?” These are some of the questions you might be asking yourself to inform your inventory and supply chain decisions.

Demand Forecasting is the process of consolidating and analyzing a pool of relevant data—historical sales data, cyclical or seasonal trends, consumer and marketplace trends and a myriad of other data elements—to project a vivid outlook of what your future sales will be. When you know how much demand there is for your products, then you can figure out how much needs to be made available. Simply put, an accurate and insightful Demand Forecast is the cornerstone of effective Inventory Management.

What is ML Demand Forecasting?

Machine Learning as a general practice enables computers to comb through vast amounts of data and learn from it, either to generate new data, provide insights or take automated actions based on that intelligence. Such algorithms are applied to a multitude of tasks today.

Extending this concept to forecasting, what this means is that all the historical sales data for all your products on Amazon, along with all their attributes, can be fed into an integrated solution—which learns all the trends and intricacies in the data, and performs all the complex calculations necessary to reliably project your future sales.

This is leveraging what computers are very good at, and very likely challenging to you, to your advantage. I imagine as an Amazon Seller, this is way more business-critical to you than changing the tone of an email you wrote using generative algorithms.

So, What’s the Big Deal about Machine Learning anyways?

There are way too many advantages to using ML algorithms and techniques to shout from the rooftops, but let’s get into a shortlist which benefits you specifically as someone running an Amazon business:

• Pattern Recognition: As mentioned further above, this is a major point a lot hinges on. Without explicitly stating what to look out for, such a system will easily classify and categorize your Amazon sales data giving you more insights into each product and its sales than you’ve ever had.

• Handling Complex Relationships: ML models are able to study links and patterns between multiple variables simultaneously, determining what’s important and what isn’t to providing the best predictive model for your Demand and Inventory forecasts.

• Continuous Improvement: The generated model keeps improving itself when new data is introduced because new data to learn from provides even more insight. Any changes in trends are also immediately recognized and this results in constant fine-tuning without user interaction.

• Automation: Because you can trust an ML system to keep learning and improving, you can also take advantage of the system to take actions you designate based on rules you define. This allows you to efficiently manage your FBA inventory by spending more time making only decisions which cannot be delegated to the platform.

How Ronin Simplifies your Amazon Inventory Management using Machine Learning

The diagram below illustrates a highly summarized version of the process Ronin performs with your data to conduct predictive analysis and automate tasks on your behalf.