Today, we’re embarking on a journey to unlock the power of AI for demand forecasting. Whether you’re a novice or expert, this detailed article will walk you through the process of conducting a Proof of Concept (POC) step by step. So, let’s dive in and explore how AI can help you predict future toy sales with ease!
Step 1: Understanding Demand Forecasting
Before we jump into the technical details, let’s understand what demand forecasting is all about. Demand forecasting is like predicting the future – but for your products! It helps businesses anticipate how much of a product customers will want to buy in the future, so they can plan ahead and make smarter decisions about production, inventory, and more. To start POC, you need to understand what factors influence toy sales in the store.
Example: Think about what makes customers rush to your store to buy toys. Is it the holiday season? Special promotions? Or maybe the latest toy craze that all the kids are talking about?
Step 2: Gathering the Right Data
Now, let’s talk about the ingredients you need for your demand forecasting recipe. You’ll need historical sales data for different toys, along with information about events, promotions, and any other factors that might affect sales.
Example: If you know that toy sales spike during the holiday season, you’ll want to collect data on past holiday sales to help predict future sales patterns.
Step 3: Identifying Important Features
In demand forecasting, not all data is created equal. Some factors have a bigger impact on toy sales than others. These are called features, and they’re like the secret ingredients that make your forecasting model work.
Example: Important features for your toy store might include past sales data, seasonal trends, promotions, and maybe even the weather (think rainy days vs. sunny days).
Step 4: Choosing the Right Model
Now it’s time to pick the right tool for the job – in this case, the right Gen AI model for your demand forecasting POC. There are many different models to choose from, but some are better suited to certain types of data than others.
Example: For your toy store POC, you might consider using a Time Series Forecasting model, which is great for predicting future sales based on past sales data.
Step 5: Training and Testing Your Model
Once you’ve selected your model, it’s time to train it on your data. This involves feeding it with historical sales data and then testing it to see how well it performs at predicting future sales.
Example: Think of training your model like teaching a new employee how to do their job. You show them examples of past sales data and ask them to predict future sales based on what they’ve learned.
Step 6: Monitoring Performance
After training your model, it’s important to keep an eye on its performance over time. This involves regularly checking how well it’s predicting toy sales and making adjustments as needed to improve its accuracy.
Example: Just like you keep track of your store’s sales performance on a daily or weekly basis, you’ll want to monitor your model’s performance to make sure it’s still making accurate predictions.
Step 7: Evaluating Your Model
Finally, it’s time to evaluate how well your model is performing. This involves comparing its predictions with actual toy sales data and analyzing any discrepancies to identify areas for improvement.
Example: Let’s say your model predicted that you would sell 100 toy cars on a rainy day, but you only sold 50. You would investigate why there was a difference and make adjustments to your model accordingly.
Why Cloud Platform Matters
Using a cloud platform like AWS, Google Cloud, or Azure is essential for running your POC smoothly. It offers scalable computing power, cost-effectiveness, and flexibility, allowing you to access the resources and tools you need to train and deploy your Gen AI models.
Example: Think of the cloud platform as your virtual assistant, providing you with all the computing power and resources you need to run your demand forecasting POC without having to invest in expensive hardware or infrastructure.
Your Turn to Engage!
Now that you have a clear understanding of how to run a POC for demand forecasting using Gen AI, it’s time to put your knowledge into action! Have you tried implementing demand forecasting in your organization before? What challenges did you face, and how did you overcome them? Share your experiences and insights in the comments below, and let’s continue the conversation!