Do You Have the Demand Forecasting Capabilities to Meet Your Omni-Channel Customers’ Demand?
There has been a revolution in retail. It’s been driven as much by consumers embracing technology as it has by innovative business models. In fact, 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.
In short, your customers have become omni-channel shoppers and they now expect you to deliver an omni-channel 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 account of 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 fulfilment point (a warehouse or store). On top of that, handling customers’ returns add 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 omni-channel demand forecasting and inventory planning. Invent Analytics 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 omni-channel element and the third is returns forecasting.
Forecasting at SKU-fulfillment time and zip-code level for each fulfillment preference:
1. Omni-channel 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 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 is now vital to reducing inventory holding costs. The return ratio for ecommerce 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 Analytics can help you reduce lost sales, improve your inventory management, and achieve highly accurate forecasts at all levels of granularity, check out our Demand Forecasting Solution.
Returns Forecasting: A Must-Have for Retailers
Retailers have always had to deal with returns. While returns can’t be avoided completely, awareness and AI-powered return forecasting is key.
Retail Demand Forecasting: What, How, and Why?
What is demand forecasting in retail really? How is it done? What do retailers need to create better forecasts? Let’s find out.
Game-changers in Retail
Returns Management: How to Turn a Costly Burden into an Opportunity
In this Game-changers in Retail Series, we sat down with Jonathan Colehower, who has over 20 years of experience in the supply chain domain.
Game-changers in Retail
The Future of Demand Forecasting and Inventory Planning for Grocery Retailers
In this Game-changers in Retail interview, we met with Makysym Tipukhov, Demand Forecasting Director, Fozzy Group, to discuss the future of grocery retail and how grocers can thrive in this new omni-channel era.