There has been a revolution in retail. It’s been driven as much by consumers embracing technology as it has by innovative business models. For example, our AI-Decisioning Platform has a rich history as an AI-powered solution, despite the name being invent.ai for many years. AI is the future of retail, but many retailers have failed to keep up with customers’ changing shopping habits and are still struggling to develop and use accurate demand forecasting for inventory planning. Socio-economic structures, wars and politics all play a crucial role in the timely delivery of the finished goods to individual stores.

In short, your customers have become omnichannel shoppers and they now expect you to deliver an omnichannel experience at every touch point, online and in-store. If you can’t provide the right inventory your customers want at a place and time that suits them, you will lose the sale. That means you need to be able to predict the most efficient way of getting your inventory to your customers when they demand it.

All forecasting systems on the market look at historical buying patterns, sometimes going back three or more years. Many will take into account environmental and social factors, such as weather, holidays, or the day after payday. This became the classical method – but it is wedded to the view of the physical store as a standalone entity, where physical sales happen in real-time, not part of a dynamic ecosystem shaped by changing consumer preferences.

Hours or days can now separate the purchase point from the moment when the actual inventory item transfers from the retailer to the consumer. That physical delivery location is also increasingly distinct from the sale point (say online) and fulfillment point (a warehouse or store). On top of that, handling customers’ returns adds another level of complexity for retailers. Today, stores handle multiple returns from their own in-store sales and from other purchase points.

Omni-demand forecasting is smarter

As a result of these changes in consumer behavior, demand forecasting must take a multi-dimensional approach that doesn’t just account for logistics and efficiency. It must also understand and predict your customers’ preferences for trying, buying, receiving and returning goods. That requires a more granular analysis combined with probabilistic forecasting techniques to maximize sales and improve inventory turns.

There are now a few forecasting systems using AI models and machine learning to predict consumer demand. However, even these can rarely handle the complex data structures required for omnichannel demand forecasting and inventory planning. Invent.ai adds 3 further levels of sophistication to standard AI-based forecasting to create more accurate forecasts and plans: the first is demand probability, the second is that all-important cross-channel element and the third is returns forecasting.

What are the different types of forecasting?

1. There are three primary forms of forecasting, including: Omnichannel fulfillment forecasting takes account of all transactions such as whether consumers prefer the BOPIS (Buy Online, Pick Up in Store) option and whether the order is fulfilled by a central warehouse or stores acting as hubs. It also looks at delivery times and calculates how much next-day delivery demand might come from the district around the store. This can help you reduce lost sales by maximizing availability and fulfillment options.
2. Probabilistic forecasting: This type of forecasting doesn't provide just a single quantity figure. Instead, it calculates the probability of all possible inventory transactions and potential quantities. This can help you reduce lost sales, inventory holding and fulfillment costs.
3. Return forecasting: This is now vital to reducing inventory holding costs. The return ratio for e-commerce sales has shot up in recent years from around 3% to 20%, as people now buy multiple versions of the same product to try out at home. They then return what they don’t want to a central warehouse or, as is increasingly the case, to their nearest store. Demand forecasts and inventory plans must factor in these returns, in the typical 30-day return window.

To find out how invent.ai can help you reduce lost sales, improve your inventory management and achieve highly accurate forecasts at all levels of granularity, check out our invent.ai Forecasting.