Industries like retail, healthcare, and manufacturing to name a few are always looking for more effective ways to predict their inventory needs. Typically this involves pouring over data and making educated presumptions.
Sometimes, businesses end up with the right amount of inventory but this isn’t always the case. Over and understocking are common issues and this is when AI forecasting can help. AI analyzes a wide range of data to help ensure your business meets demand. However, implementing successful AI demand forecasting strategies is a crucial part of the process.
So, how do you successfully implement AI demand forecasting? Here’s a look at some key strategies to help ensure you’re receiving accurate industry predictions.
AI can only do so much if you’re objectives aren’t clearly outlined. For example, you may want to know how many pairs of sandals to stock for the spring and summer season but instead you get a list forecasting sales for all four seasons.
Yes, this information can be useful but it also takes time to go through it. By the time you find the forecasted sales on sandals, it may be time to start thinking about your fall inventory. Identifying the specific products, areas, or services and setting goals you can easily measure is important here.
Yes, AI forecasting can make it easier to identify trends and ensure you’re not over or understocking inventory. However, artificial intelligence isn’t perfect—and mistakes can often pop up.
Some errors may be the result of not setting the correct parameters. Remember the sandal example from earlier. If you don’t set parameters, AI may collect data from every sale instead of focusing on sandals and other types of footwear.
Other times, AI can get a little lost and collect the wrong data. To ensure your predictions are realistic, it’s a good idea to gather your past data. This often includes your past sales, customer behaviors, and market trends. You can find online data correlation tools to help you make sense of the information.
Compare it to what AI demand is forecasting. If there are discrepancies, you know you need to make improvements to the parameters you’re setting.
Artificial intelligence is always learning. If your first attempt at implementation isn’t successful, don’t give up. Instead, it’s time for a little more training. Thankfully, this isn’t difficult. In other words, you’re not going to need to pull your entire IT department away from other tasks just to focus on training your AI demand forecasting model.
Collect your historical data and separate the information into two categories, training and validation. One set is used to train the AI model and the other is to rate its performance. As you run each training model, make small adjustments to the parameters until you receive accurate results.
Okay, this is probably when you’re going to need to put your IT department to work. If you don’t have an IT department or team members familiar with AI models, it’s probably time to partner with experienced professionals. You may even need to bring a data scientist or two on board.
Yes, this can be costly but the results are usually worth the expense. Once the AI model is integrated with your IT infrastructure, you can take advantage of demand forecasting.
Where you deploy the AI model depends on your business. If you're primarily in the cloud, then the AI model is integrated into your virtual servers. The same applies to your hardware or if you’re using a mixed model.
Some of the data will come from past sales and other metrics but it’s also a good idea to be able to forecast demand in real time. This allows you to make instant decisions that can help grow your business. Integrating real-time data into your AI model can be a little tricky, but you also have options on how you accomplish the process.
You may be able to simply set up data pipelines—think of it as something similar to a water pipe. Instead of liquid flowing, it’s current data. You can also take advantage of APIs, which is often an effective way of ensuring the AI model is receiving current and accurate data.
Even with extensive training, you still want to continuously test and validate the data. Remember, even AI models can get something wrong, and testing helps guarantee that you’re receiving valid data.
After all, you don’t want to overstock your inventory, but you also don’t want to run out. Regular validation and adjustments can help maintain the accuracy of your AI models, allowing you to make better-informed decisions and keep your inventory levels optimal.
