How to Enhance Inventory Management with Real-Time Data Strategies

Rami Pinto Prieto and Tamar Alphaidze

In the competitive retail landscape, having the right stock in the right place at the right time is crucial. However, the retail industry faces significant challenges in achieving this goal. In 2022, unsold stock in the US surged by a staggering $78 billion, reaching approximately $740 billion—a shocking 12 percent increase.

Without a single view of inventory, retailers struggle to compete with new market disruptors offering customers omnichannel experiences. Retailers who get stock management right can move to distributed supply chains, leveraging stock across online and in-store platforms to distribute inventory quickly and react to shifting buying patterns. With effective access to the data, retailers speed up workforce efficiency and allow for automation.

In this blog, we will explore how inventory management affects customer experiences, effective stock management for accurate demand forecasting, and workforce productivity.

Building a single view of inventory to enhance customer experience

Modern retail consumers expect seamless omnichannel experiences, like the ability to view product availability online and pick it up at a nearby store the next day. They will gravitate toward retailers that prioritize their need for convenience and speed.

The difficulty in delivering these features often stems from the lack of a centralized inventory hub, i.e. operating with separate inventories for online and in-store. Combining data from diverse sources, including vendor solutions, RDBMS databases, and files, becomes a complex task that hampers the ability to achieve an accurate real-time view of stock availability. It also extends the time to market for new features, requiring redundant and customized development efforts across different channels. This lack of adaptability impacts the retailer's ability to offer customer-centric features, putting them at a disadvantage compared to their competitors.

To track inventory in real-time and improve visibility and consistency across multiple channels and locations, MongoDB’s document data model is a powerful choice. Using the document model, data types can be combined easily, making it more flexible for handling diverse product data. Its intuitive design enables developers to iterate on the data model at the same pace as the rest of the code base, without downtime for schema changes. This agility accelerates the implementation of new features and functionalities that can be built on top of a single view of inventory, like real-time stock availability, and buying online and picking up in-store the next day.

Diagram of enabling buy online and pick up in-store through single view inventory. One the left, the diagram shos the customer connecting to the ecommerce store, which is connected to the online inventory. The store inventory displays what is available across multiple stores. On the right, the diagram shows how the ecommerce store is able to display a single view of the inventory from multiple stores.
Figure 1: Enabling buy online and pick up in-store through single-view inventory

By leveraging a single view of inventory, retailers can accelerate the development of superior customer experiences, securing a competitive edge in the retail industry.

Effective stock management with real-time analytics

Now that the retailer can see and understand inventory levels across their organization in one place, they can begin to manage stock more effectively. This enables retailers to move to a more complex distributed supply chain and activate the use of real-time analytics or AI.

In a traditional retailer without a centralized inventory management system, the complexity of mixing stock between channels was too difficult in a segmented data landscape, leading to waste through dead stock in stores while others or online channels have an insufficient supply of the same item.

With a single view of inventory, items can be moved around in a way that makes sense for the business. Online orders destined for in-store pick-up might be packed using in-store items. Dead stock on a shelf might be available online. Stores can move stock between themselves in an intelligent manner.

The added complexity does come with more complex decision-making. It's vital to be able to ask difficult questions about the inventory management system and get answers in real-time. Rather than move data to a different analytical platform and get answers a day later, retailers are looking to do real-time analysis to make important stock allocation decisions in real-time.

Next, retailers tackle demand forecasting and bring intelligence into stock allocation. This is where a translytical data platform comes in. Its distributed architecture means analytical workloads can run on a real-time analytics node. This approach eliminates the need for additional systems such as separate analytics platforms and reduces the lag associated with transferring data. The aggregation framework, MongoDB’s advanced processing pipeline can then be used to ask complex analytical questions and get results back to the user in real-time.

For example, retailers can easily see which products are the most popular or the most likely to run out of stock soon or understand when a product rapidly sells out in one store if this is a trend or tied to a specific event like a sports game. This insight can guide smart decisions on redistributing products to get them in front of the customer who is most likely to buy.

Diagram of inventory real-time analytics. The inventory dashboard uses real-time analytics to display the current inventory count and uses ACID transactions to complete orders.
Figure 2: Inventory real-time analytics

This architecture could also be leveraged to feed AI or machine learning models. The more complex the supply chain becomes, the more retailers are turning to cutting-edge technology to gain further insight. Demand forecasting is a great use case for AI as there can be a vast amount of possible factors and results. With MongoDB, retailers are integrating AI systems so they can access real-time data, enhancing their accuracy and responsiveness. This synergy enables businesses to streamline their supply chains.

Boost workforce efficiency through an event-driven solution

A successful inventory management strategy also contributes to improving workforce efficiency. The lack of real-time updates brings on inefficient inventory tracking procedures that result in errors, such as excess or unavailable goods, and hinder customer orders, leading to dissatisfaction among staff and customers alike. As the business grows and sales volume increases, the ability to process large amounts of real-time data becomes increasingly important. A future-proof, scalable, and flexible architecture supporting the tools that empower your workforce, can make a difference when retailers face a peak in demand or decide to expand their business.

The central question retailers face is, "How can businesses enhance workforce efficiency in their inventory operations?

The key lies in using event-driven architectures for managing inventory systems. MongoDB is a great fit for this approach, offering features like Change Streams, Triggers, and the Kafka Connector. Take for example the scenario seen in Figure 3; a customer purchases a t-shirt in-store. The Point of Sale device then instantly updates the product stock. If stock runs low, this change is instantly sent to the store manager app through Change Streams to alert the store manager. To automate the re-ordering process, MongoDB Triggers can be set up to trigger a function that would perform complex actions in response to the event, like automatically reordering products.

Diagram of event-driven architecture for inventory mangement. The point of sale device, used by the customer, activates the purchase event with the Inventory management platform. The platform utilizes triggers and change streams to connect with the back-end systems like automated replenishment and low stock alert.
Figure 3: Event-driven architecture for inventory management

Today, when an influencer mentions a particular item, it can fly off the shelves at an unforeseen pace. Thanks to automation enabled by event-driven architectures, such situations become opportunities, not challenges. As soon as that item goes unexpectedly out of stock, the system triggers an automatic reorder, ensuring that your shelves are replenished in real-time. This rapid response eliminates the need for manual intervention, freeing up your store manager to focus on more value-add activities. Instead of spending hours every day reordering items, they can now dive into more engaging tasks, like interacting with customers, providing personalized recommendations, and exploring innovative stock decisions.

This isn't just a theoretical advantage. A prime example comes from MongoDB’s work with 7-Eleven. By implementing a custom inventory management app, 7-Eleven streamlined its operations across 10,000 stores in the U.S. and Canada. With event-driven functionality, 7-Eleven store employees can now seamlessly manage transactions, sales, and inventory through mobile devices, eradicating the need for manual updates and improving overall workforce efficiency.

Closing the loop for a future-proof inventory management strategy

Effective inventory management strategies are vital in the evolving retail landscape. By providing a consistent single-view inventory, retailers can enhance customer experiences and gain a competitive edge. With efficient stock management capabilities, they can optimize their inventory levels, reducing costs and improving profitability. And by embracing event-driven solutions, retailers can boost workforce efficiency, enabling data-driven decision-making and streamlining processes through automation.