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For retailers selling online and offline, artificial intelligence is transforming inventory availability management and order fulfillment.

This article was written and paid for by Fluent Commerce. This content is not WIRED editorial content. The content does not necessarily reflect the views of WIRED, its affiliates or owners, nor does it reflect any direct or indirect endorsement by Fluent Commerce or Business Reporter , its affiliates or other customers.Artificial intelligence is a major trend in business right now. The value of AI in the retail industry is expected to grow from $3.7 billion in 2021 to $16.8 billion in 2030, a healthy compound annual growth rate (CAGR) of 15.7%.

The use of artificial intelligence in customer-centric areas such as advertising and communications is well known. One only has to think of today’s automated chatbots to see the important role AI is already playing in retail customer service.
One area that is less widely known is the use of artificial intelligence in inventory levels. But in fact, inventory management ranks second only to customer service as a use case for AI< /span>, with nearly half of retailers (47%) saying AI can greatly enhance inventory management by tracking inventory online and at physical locations, facilitating true Provide customers with an omni-channel experience.
Transform inventory and order management

Clearly, there are significant opportunities in inventory availability and order management, where AI can help businesses become more efficient and maximize profits. Let’s look at a few.
Inventory availability optimization

Overstocking and understocking are two problems any retailer wants to avoid. Artificial intelligence helps retailers optimize inventory levels, improve efficiency and profitability. Demand forecasting is a particularly powerful tool here: AI predicts future demand based on historical data and other factors, allowing orders to be routed to the best locations to maintain optimal inventory levels.

Demand sensing is another important use case. This involves short-term demand forecasting, which can alert you if the stock status of a SKU is at risk of being out of stock, or if order sourcing rules are inappropriate based on current stock.

AI can also help manage safety stock. Safety stock is additional inventory kept to reduce the risk of stockouts. However, in some cases, inventory buffers can lead to sell-offs. Artificial intelligence enables dynamic safety stock by constantly checking inventory status, current demand and forecast sales, then automatically updating safety stock levels. The technology can improve inventory turns across the entire retail point network (online and offline).
Another advantage of AI is that it can improve procurement logic. Order sourcing involves sending orders to the best locations based on the retailer’ ;s business goals. These may include fast delivery; reducing partial shipments; shipping costs; or shipping from locations with the slowest inventory movement to avoid markdowns. By optimizing purchasing logic, retailers can enhance profitability, improve inventory turns, reduce markdowns and inventory waste , and enhance sustainability.

Typically, order sourcing is managed through simple purchasing rules, such as shipping from the location closest to the customer. Artificial intelligence provides valuable opportunities to quickly use richer data sets. This can include location attributes such as labor capacity, the maximum number of open orders the location can handle, shipment damage rates, and average order processing speed. or product attributes, such as whether the item is fragile, bulky, or must be shipped separately. or location-specific inventory attributes, such as inventory duration, sell-out rates, or likelihood of price reductions. By uncovering complex data patterns, order procurement can be transformed into a strategic process that drives sales and improves customer satisfaction.

Logistics optimization
To achieve the most efficient fulfillment operations, logistics processes must be optimized. This may include consolidating orders using existing trunk routes, minimizing cross-docking or holding times.

Order management and tracking are at the heart of this. AI can track multiple orders in real time and can identify inventory issues, production delays and delivery bottlenecks. AI models can also incorporate other data, such as telematics, into the analysis to optimize shipping routes and ensure timely delivery of orders.
Achieve success with artificial intelligence
These are huge opportunities for retail. However, being successful with AI requires knowing the right questions to ask the AI ​​​model. You need to your business metrics and the improvements understand you want to make.

For example, do you want to increase inventory turns, reduce multiple inventory moves, or improve cross-selling by ensuring that new and popular product mixes are readily available? Once youknow the questions you want to ask, you will be able to identify the data you need to provide the answers.
Data quality
Often the questions that you can answer with traditional systems are limited by the data that is available. Using AI-powered systems uncovers new opportunities to use data sets that were not available before.But even AI models need data of sufficient quality. And unfortunately, when it comes to order and inventory processes, data of sufficient quality can be hard to find.
Inventory data is often poor quality. It may sit in multiple systems, stored in different formats, much of it poorly structured, and some of it is incomplete or polluted by inaccurate or out-of-date information. In addition, it may contain irrelevant data that will cause AI systems to deliver biased outputs.
Providing quality data to train and operate AI models is a challenge. For most AI projects, approximately 80 percent of the cost is getting the data right. And in many cases, even after a lot of effort, organizations find they don’t have the right data. So, the project fails before it is launched.

Finding Good Data
The data you need will depend on the questions you want to ask. This means being able to capture the right data and make it available to the AI ​​​model.
A modern order-management system such as Fluent Order Management can provide a continuous stream of sales data points on demand, together with related contextual data, including location (capacity or opening hours, for example), order (order date, delivery date), product (weight, fragility), and customer (credit status, return rate). This contextual data can be extremely valuable and should be stored, not purged or condensed, so it’s available for future analysis.
A Seamless Experience
The role of inventory availability and order management is to deliver a seamless omnichannel experience for consumers while bolstering retailer profitability. Modern, event-based systems such as Fluent Order Management capture all the data signals that enable retailers to take full advantage of AI models.
What’s more, it provides short-term value as well. This enables retailers to get an accurate real-time view of their inventory so they can increase fill rates and reduce the number of orders that are canceled because of delays.
With Fluent Order Management, inventory availability and fulfillment processes can be managed by region or channel to enable growth and support local needs. And order management processes can be integrated with other business systems that need to be aware of inventory levels. For example, advertising platforms can be managed so that investment is not wasted on advertisements for out-of-stock items.

Alongside advances in customer experience, such as chatbots and personalized shopping, today’s AI-enabling inventory availability and order management systems are enhancing retail profitability by maximizing sales, increasing fulfillment speed, and minimizing waste.
To find out more about how AI can transform your inventory availability and order management, visit fluentcommerce.com

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