Building an event-driven inventory management system

Solutions overview

Executive summary

In the competitive business landscape of retail and manufacturing, having the right inventory of goods in the right place at the right time is crucial. Insufficient inventory can lead to operational disruptions and missed opportunities, while excess inventory can increase costs and risks associated with storage. Companies of all sizes struggle with inventory management. Solutions such as a single view of inventory, real-time analytics, and event-driven architectures can help businesses overcome these challenges and elevate their inventory management to the next level. This demo will guide you through the process of building an inventory management system with all the capabilities mentioned above, tailored for diverse industries such as retail and manufacturing.

Overview

Effective inventory management is critical to ensure operational success and customer service excellence. With the increasing complexity of supply chains and rising customer expectations, businesses need robust systems to track inventory levels, automate processes, and analyze trends in real time. As industries continue to evolve, the importance of integrated and event-driven inventory solutions becomes more pronounced as they allow businesses to respond swiftly to market demands, minimize costs, and maintain competitiveness.

Businesses must adopt strategies that allow for dynamic adjustments to inventory levels, optimizing stock management, and reducing inefficiencies. Automation and real-time data integration are key components of these strategies. They provide the capabilities needed to enhance operational agility, streamline supply chain processes, and ensure timely delivery of products.

Inventory management in the retail industry

In today’s fast-paced retail landscape, ensuring your inventory is in the right place at the right time is essential. However, the retail industry faces significant challenges in achieving this goal. In 2022, unsold stock in the U.S. surged by a staggering $78 billion, reaching approximately $740 billion — a shocking 12 percent increase.

Retailers today need a unified view of their inventory to meet customer expectations for immediate availability and seamless purchasing experiences. The disparity between online and in-store stock can lead to missed sales opportunities and customer dissatisfaction.

Effective stock management enables retailers to leverage distributed supply chains, moving inventory fluidly between channels to meet demand where it arises. This reduces the risk of dead stock in physical stores and stockouts in online channels, enhancing overall inventory efficiency. Real-time analytics empower retailers to make data-driven decisions, adapting quickly to changing market conditions and consumer behavior. Furthermore, automated processes reduce manual errors and free up staff to focus on value-added activities, such as enhancing service quality. Event-driven architectures facilitate these improvements, allowing for seamless inventory data integration and synchronization across various platforms and devices.

Inventory management in the manufacturing industry

Modern manufacturing supply chains are complex, interconnected systems. Efficient supply chains can control operational costs and ensure on-time delivery to customers. Inventory optimization and management are key components in achieving these goals. While maintaining higher inventory levels allows suppliers to deal with unexpected fluctuations in demand, they come with higher inventory-holding costs that may be passed on to customers. Thus, every player in the supply chain is willing to strike a balance between inventory levels to maximize profitability and competitive advantage in the market. Effective inventory management mitigates the risk of the bullwhip effect, where sudden demands can disrupt the supply chain costs and performance.

A significant example of inventory management challenges in manufacturing is relative to overstock. Nearly 8% of surplus stock globally will ultimately end up as waste, with about $163 billion of inventory discarded annually. For manufacturers, managing and optimizing inventory levels starts with maintaining an accurate and real-time view of inventory levels across multiple plants, warehouses, and suppliers. This is essential because it is impossible to optimize without visibility into current inventory levels.

Real-time data integration helps manufacturers track inventory movements and optimize stock levels, but it also is essential for automation. Automated alerts and workflow triggers maintain inventory levels without constant manual oversight. This improves accuracy and allows manufacturers to respond swiftly to changes in production needs or supply chain disruptions, maintaining a smooth operation.

Closing the loop for a future-proof inventory management strategy

MongoDB provides a leading component for modern inventory solutions. We help businesses enhance service quality, workforce efficiency, and optimize stock management by enabling a single view of inventory, event-driven architectures, and real-time analytics. This solution lays the groundwork for advanced scenarios such as integrating IoT and RFID tags, delving into AI/ML forecasting for precise demand prediction, and distributed logistics. The versatility of this solution allows its application not only from warehouse to point of sale but also across the entire supply chain, including manufacturing, transportation, retail, and reverse logistics, making it a valuable asset for diverse business domains.

Reference Architectures

With MongoDB:

Our foundation is a dynamic Next.js application seamlessly integrated with the robust capabilities of MongoDB Atlas.

Within the MongoDB Atlas ecosystem, the MongoDB database comprises four collections (products, transactions, users and locations) and one view (product_area_view). The data retrieval and manipulation of our database is elevated by Atlas Search and Facets. Additionally, the architecture leverages Triggers and Functions to enhance responsiveness and real-time interaction.

Diving deeper, the MongoDB database communicates with our Next.js backend via the MongoDB driver for Node.js, fostering swift and efficient communication. Similarly, the MongoDB database interfaces with the Next.js frontend via the Data API and Change Streams provided by Atlas App Services.

Atlas App Services acts as a smooth pathway that allows information to move seamlessly between the user-facing part of our app (Next.js front end) and the storage part (MongoDB database). This ensures that updates happen on time. Change Streams play the role of an event listener, always paying attention to any changes in the data and quickly sending those changes to the frontend so that everything stays up to date in real time.

This synthesis ensures a harmonious interaction between the application’s layers, facilitating swift, responsive, and intelligent data management and presentation. Take for example the scenario of an outbound transaction that makes a product go below the optimal stock level. This event is sent immediately through Change Streams to alert the inventory manager. To further automate the process, MongoDB Triggers can be set up to execute a function that would perform complex actions in response to the event, like automatically reordering products.

Inventory Management system

Data model approach

The primary collections — products, transactions, users and locations — form the foundation of our inventory management system. These collections represent your stock, incoming and outgoing movements that affect stock levels, your app users, and the different locations that the inventory system is serving. In addition, we have a view that is used by the area manager to have an overview of the inventory in all the locations in the same area.

What sets the document data model apart from traditional tabular models is its remarkable flexibility, making it the perfect choice for achieving a single view of inventory. With a tabular approach, achieving a comprehensive overview of your inventory would typically involve complex joins across multiple tables. However, with MongoDB's document model, this complexity is eliminated.

For instance, in the transactions collection we take advantage of the Extended Reference pattern when referencing an item. We not only include the product.id, but also the product.name to be displayed in the UI without the need of a join or an extra query to the products collection.

If we now consider the products collection, we see how it encompasses what would have otherwise demanded over 10 tables in a relational system. For example, the product variants are contained as an embedded array in the same document, and other attributes such as product stock or locations would traditionally require additional tables. This transformational power stems from the document model's inherent flexibility. It not only empowers a more intuitive design but also ensures that related data is stored together, optimizing access.

Equivalent relational model for products

Building the solution

Building this solution involves six major steps, encompassing MongoDB Atlas setup, your app configuration, and optional personalization steps.

• Step 1: Replicate the Demo Database. Create a MongoDB Atlas cluster and replicate the collections, views, and indexes from the demo database.

• Step 2: Import the App Backend with Atlas App Services. Create a new app in Atlas App Services, linking it to the MongoDB Atlas database. This will allow us to make use of Change Streams for real-time low-stock alerts and triggers for auto-replenishment workflows.

• Step 3: Enable full-text search and filter facets. Configure search indexes for full-text search and filter facets in the products and transactions collections.

• Step 4: Set up real-time analytics with Atlas Charts. Enhance your application's visualization and analytics capabilities with Atlas Charts. Unlock real-time analytics and create custom charts based on your preferences.

• Step 5: Configure your app frontend. Clone the GitHub repository to your local machine, update the environment variables with your own values, install the dependencies, and run the app locally on http://localhost:3000.

If you want to get hands-on, follow our step-by-step tutorial on how to Build an Inventory Management System using MongoDB Atlas. Access our GitHub repo for code samples, deployment guide, and more!

Key learnings

Throughout this solution, we’ve covered multiple MongoDB topics. Here’s a quick recap of the key learnings:

• Real-time alerts: Implement real-time low-stock alerts in your frontend using Change Streams.
• Workflow automation: Leverage database triggers to automate stock replenishment workflows.
• Real-time analytics: Stay on top of your data, analyze trends, and make informed decisions in a timely manner with Atlas Charts.
• Single view of inventory: Take advantage of the flexibility of the document model to create a comprehensive single view of inventory.

Remember that this is just the beginning. Feel free to explore, tweak, and enhance your inventory management system to fit your own needs.

Technologies and products used

MongoDB developer data platform:

• Atlas App Services
• Atlas Search
• Aggregation Framework
• Change Streams
• Atlas Charts
• MongoDB Node.js driver
• Data API
• Triggers
• Functions

Partner technologies:

• Next.js
• React

Author

• Ramiro Pinto Prieto, MongoDB
• Tamar Alphaidze, MongoDB