Powering inventory availability and order management with AI

 

The use of AI in customer-focused areas such as advertising and communications is well known. And one only has to think of today’s automated chatbots to see the major part that AI is already playing in retail customer service.

 

One area that is less well publicised is the use of AI in managing stock levels. But, in fact, inventory management is second only to customer care as a use case for AI, with nearly half of retailers (47 per cent) saying that AI can greatly enhance inventory management by tracking stock online and at physical locations, facilitating a truly omnichannel experience for customers.

 

 

There are clearly significant opportunities in relation to inventory availability and order management where AI can help businesses increase efficiency and maximise profits. Let’s take a look at a few.

 

 

Overstocking and understocking are two issues that any retailer wants to avoid. AI helps retailers optimise inventory levels, enhancing efficiency and profitability. Demand prediction is a particularly powerful tool here: AI predicts future demand based on historical data and other factors, enabling orders to be routed to the best location to maintain optimal stock levels.

 

Demand sensing is another important use case. This involves short-term demand forecasting that can alert you if an SKU’s inventory position is at risk of becoming out of stock or if an order sourcing rule is inappropriate based on the current inventory.

 

Something else that AI can help manage is safety stock. Safety stock is additional stock that is held back to reduce the risk of a stockout. However, in some circumstances, stock buffers can cause underselling. AI enables dynamic safety stock by constantly examining inventory positions, current demand and predicted sales, and then automatically updating safety stock levels. This technique can increase inventory turnover across an entire network of retail locations, online and offline.

 

 

Another strength of AI is that it enables sourcing logic to be improved. Order sourcing involves routing orders to the best location based on a retailers’ business objectives. These may include: fast delivery; reducing split shipments; delivery cost; or shipping from the location with the slowest-moving inventory to avoid markdowns. By optimising sourcing logic, retailers can strengthen profitability, increase stock turnover rates, reduce markdowns and stock wastage and enhance sustainability.

 

Normally order sourcing is managed with simple sourcing rules – for example, to ship from the location closest to the customer. AI provides a valuable opportunity to use a richer data set, at speed. This can include location attributes such as labour capacity, the maximum number of open orders a location can handle, shipment damage rate and average order processing speed. Or product attributes, such as whether an item is fragile or bulky, or must ship alone. Or inventory attributes at a specific location, such as inventory age, sell-through rate, or the likelihood of markdowns. By uncovering complex data patterns, order sourcing can be transformed into a strategically significant process that will boost sales and bolster customer satisfaction.

 

 

To enable the most efficient fulfilment operations, logistic processes must be optimised. This may include order consolidation using existing trunk routes, minimising cross docking or minimising staging time.

 

Order management and tracking is central to this. AI enables real-time tracking of multiple orders and can identify inventory issues, production delays and delivery bottlenecks. AI models can also factor other data into their analysis, such as telematics, to enable the optimisation of transport routes and ensure timely order deliveries.

 

 

These are huge opportunities for retail. But achieving success with AI involves knowing the right questions to ask AI models. You need to understand your own business metrics and the improvements you want to make.

 

For example, are you looking to increase inventory turnover, reduce the amount that inventory is moved more than once, or improve cross-selling by ensuring that newly popular product combinations are readily available? Once you know the questions you want to ask, you will be able to identify the data you need to provide the answers.

 

 

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 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 per cent of the cost is getting the data right. And in many cases, even after a lot of effort, organisations find they don’t have the right data. So, the project fails before it is launched.

 

 

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 datapoints 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.

 

 

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 cancelled because of delays.

 

With Fluent Order Management, inventory availability and fulfilment 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 adverts for out-of-stock items.

 

Alongside advances in customer experience such as chatbots and personalised shopping, today’s AI-enabling inventory availability and order management systems are enhancing retail profitability by maximising sales, increasing fulfilment speed and minimising waste.