Improving Demand Forecasting with New Techniques

Forecasting demand is a bit like trying to predict the weather. You think you’ve got it figured out, and then a sudden storm hits-like a holiday sale or a big promotional event-and everything goes sideways. These moments, which we call Peak Events (PEs), send demand shooting up high like a rocket and then crashing back down just as fast.

When it comes to these peaks, traditional methods, including some fancy neural networks, tend to get a bit overexcited. They see that demand spike and think, “Wow! This is the new normal!” So, they carry that heightened demand into the days and weeks that follow, leading to forecasts that are way off the mark. Imagine thinking you need a mountain of ice cream because you had a party, only to be left wondering what to do with all the leftovers when the party ends.

To tackle this not-so-small challenge, we’ve come up with a clever tool called Split Peak Attention DEcomposition. Yes, it sounds fancy, but it’s all about keeping it simple-like dividing your ice cream into “party time” and “normal time.” By treating the peak event as its own separate thing, we can make better forecasts for regular times, and keep the confusion to a minimum.

#The Brain Behind the Operation

Our new model works by using two main tricks: masked convolutions and a special Peak Attention module. The masked convolutions act like a filter at a coffee shop. They stop all the frothy milk from getting into our regular coffee, allowing us to focus on just the basics-the actual demand data without the peaks.

Meanwhile, the Peak Attention module is like that friend who reminds you that you still have cake left after the party. It keeps track of those peaks and lets us know what’s important during those exciting moments. Instead of letting everything get lost in the noise, this module makes sure we stay sharp and focused.

#Results That Speak Volumes

Testing our new model on a massive dataset covering hundreds of millions of products, we saw some pretty impressive results. When PEs were involved, we managed to improve accuracy during these events while also reducing the forecast errors that followed. It’s like finally figuring out how to ride a bike without wobbling all over the place-no more crashed forecasts!

This is crucial, especially for large retailers who need to know how much stock to bring in. If they guess wrong, it can lead to empty shelves or mountains of leftover products. We all know what happens next: the dreaded markdown sales that no one wants to see.

#Why Does This Matter?

Good forecasting during PEs means fewer headaches down the road, leading to better inventory management. Picture this: if a store knows exactly how many products to stock during a big sale, they can ensure there’s just the right amount for everyone. It’s like knowing the perfect number of cupcakes to bake for a party-no one goes home empty-handed, and there are no sad leftovers.

But it’s not just about pastries and products. Accurate forecasting reduces costs. When a store has too much product, it can lead to expensive storage fees and wasted resources. On the flip side, running out can mean missed sales and unhappy customers. Our new model aims to help retailers find that sweet spot.

#The Technical Bits-But Not Too Technical

We designed this model, Split Peak Attention DEcomposition, to work by breaking down the data into two parts: what happened during the peaks and what happened at other times. It’s like keeping track of two separate notebooks-one for your regular notes and another for when your friend’s band comes to town.

Instead of trying to understand all the noise, our method focuses on what’s essential during those peak moments. The use of causal indicators helps recognize when a peak will occur and masks out those moments. This way, the algorithm won’t get sidetracked by every little spike in demand.

#A Better Way to Forecast

Forecasting accurately means considering various factors-not just what happened in the past, but also what’s on the horizon. For instance, knowing when sales, holidays, or promotions are coming up can drastically shape predictions. Our model takes this into account by using past sales data alongside static information about products.

This step is crucial. Imagine a store selling winter coats during summer-nobody wants to buy a heavy jacket during a heatwave! But with our model, retailers can plan ahead even for out-of-season sales, ensuring they’re ready when customers come looking. It's all about anticipating needs and being ready for whatever comes next.

#Peeking into the Future

While our model shows great promise, we believe there’s still room for improvement. Currently, the system relies on past indicators to forecast future demand. However, incorporating newer techniques that look at data without prior assumptions may lead to even smarter predictions. This could help in recognizing peaks before they even happen!

Consider it akin to reading the tea leaves instead of waiting for the news. Being ahead of the game is always a win!

#The Takeaway

In a world where demand can shift in the blink of an eye, staying one step ahead is essential. The Split Peak Attention DEcomposition model offers a promising step in the right direction, allowing us to forecast more accurately, especially during those crucial peak events.

By splitting demand into manageable parts, filtering out unnecessary noise, and paying special attention to those important spikes, we can better serve businesses and their customers. Just think of it as planning the perfect party-ensuring there’s enough cake for everyone, without needing a second fridge to store leftovers.

With better forecasting methods, we pave the way for smarter decisions, fewer errors, and ultimately happier customers. After all, who wouldn’t want to avoid the stress of overstock or missed sales? Let’s keep those shelves stocked and the sales flowing! 🎉