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Real-Time Order Management: The Key to Streamlining Your Business

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Order management systems (OMS) have evolved from keeping paper records and spreadsheets to using web-based platforms that can automate manual tasks, minimize errors, and optimize inventory levels. In today's fast-paced business environment, managing orders effectively and efficiently is crucial for any organization. An order management platform is a software system that helps businesses manage their orders—from order initiation to order execution—across different sales channels, such as online stores, marketplaces, or physical locations. 

There are numerous touchpoints and integrations, which need a consistent, up-to-date view of data at all times in order to smoothly orchestrate order fulfillment. Each of these integrations also requires and act upon different sets of data products, which need to be high quality, trustworthy, and processed with the right context. 

Order management systems are integral components across diverse industries, spanning from retailers and logistics and distribution firms to supply chain companies. Given that each business possesses distinct process workflows, including multi-channel strategies, payment gateways, and backend system integrations, a tailored implementation of the OMS becomes essential. 

→ Learn how stream processing creates high-quality, reusable data streams.

Business challenges

Despite the benefits an order management platform brings, there are key challenges that businesses may face during the process, from order placement and validation to inventory confirmation to fulfillment: 

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Data gaps and inconsistencies: Data is a valuable asset for businesses as it allows you to discover insights and optimize your business based on historical order management trends. 

The primary challenge that businesses face in managing their orders is staying on top of inventory levels. An effective order management platform ensures your businesses have accurate and up-to-date information about the stock levels at all times. Data inconsistency leads to discrepancies between inventory levels and new orders, which can result in order fulfillment problems that impede business productivity. By automating inventory tracking and providing real-time visibility into stock availability, businesses can avoid overselling or running out of popular products. 

Fraudulent activity: Another critical aspect of order management is validation of order authenticity. Authenticating orders contributes to building and maintaining trust with customers. With the proliferation of multi-channel transactions, businesses need robust systems in place to identify and prevent fraudulent activities. The majority of order management systems do not support native integration capabilities with robust fraud detection tools that utilize intelligent algorithms and machine learning capabilities to analyze customer behavior patterns and detect potentially fraudulent orders. Ensuring order authenticity not only protects businesses from financial loss but also safeguards your reputation.

Customer disconnect: Additionally, an efficient and reliable order management platform is crucial for businesses looking to enhance their customer experience. Without the ability to leverage real-time order data, businesses may struggle to streamline operations to provide a seamless buying journey for customers. Failure to analyze customer data, purchase history, page navigation, and search keywords can also result in missed opportunities to provide personalized recommendations.

Technical challenges

  • Inconsistency due to disparate systems with varying data formats. This can result in duplication and data errors hampering decision-making. 

  • Batch processing of data introduces gaps in data and impacts order authenticity validation. Data processing in batches introduces risk of losing data between batches, which can be difficult to detect and validate. 

  • Manual data entry and inaccuracies in order processing and subsequent delay in fulfillment results in customer dissatisfaction, lost sales, and a damaged reputation.

Data streaming solution with Confluent

With Confluent, businesses can now power their order management platform with real-time data to streamline operations and provide a seamless end-to-end buying journey for customers. 

Confluent Cloud is a fully managed data streaming platform based on Apache KafkaÂź. It provides businesses with a highly scalable, resilient, and performant platform for building real-time streaming data pipelines and streaming applications. In this blog, we will explore how to overcome the challenges of an order management platform using Confluent.

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Data accuracy: To build resilience and agility, companies should look to redesign supply chain flows and handle data pipelines across systems in real time. Confluent offers robust capabilities for data integration and transformation. It allows businesses to connect disparate systems and applications seamlessly, eliminating the need for manual data entry or complex integrations. This ensures accurate and consistent data across the entire order management ecosystem. Confluent offers 120+ pre-built connectors that allow you to easily integrate with popular data sources and sinks. The connectors are designed to move data in and out of Confluent clusters—an effortless task—giving you more time to focus on core process development.

Order authenticity and fraud detection: Traditional order management systems lack the fundamental capability to identify authenticity of the orders placed through various channels. Fraud detection needs to consider various data sources, including transaction data, customer behavior, and device/login data. Confluent enables real-time stream processing of transactions, which is crucial for detecting fraudulent activities as they occur. Confluent allows datasets to be merged in real time to apply correct risk scores, making the fraud detection process more context-aware and accurate. In addition, Confluent supports the integration of machine learning models for fraud detection. These models can process large datasets with many variables and help find hidden correlations between user behavior and the likelihood of fraudulent actions. 

Confluent offers fully managed streaming tools like Flink and ksqlDB that makes stream processing extremely powerful and efficient. Stream processing enables you to build in-stream detection and analysis of order authenticity through pattern matching as the first layer of defense. Confluent’s rich ecosystem of pre-built connectors helps you feed real-time, enriched data to any number of third-party fraud detection systems like Seon, Cobalt Insight, or LexisNexis for advanced behavioral analysis. 

→ Read the ebook on using streaming for real-time fraud detection.

Solution implementation 

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The above diagram provides an overview of the deployment architecture of the streaming data pipelines in Confluent Cloud. 

  • Confluent clusters are deployed on the cloud provider of choice (e.g., AWS, Azure, GCP). 

  • Order details, customer and inventory details are captured from the source databases to the topics by leveraging Confluent’s fully managed PostgreSQL and Oracle CDC Source Connectors. 

  • Data in the topics are processed with streaming platforms like ksqlDB/Flink for real-time transformation. Orders, customers, and product data are joined to get order enriched data for further operation.

  • Order-enriched data is validated against inventory data before sending out order confirmation email notifications. 

  • Notifications are sent out using Python clients hosted on AWS Lambda, leveraging Confluent’s fully managed Lambda Sink Connector. 

  • Order-enriched data is also utilized for order aggregation to validate order flooding using the streaming tools. In addition, order information is delivered to third-party fraud prevention systems through API calls via Confluent’s HTTP Sink Connectors.

Here are stream processing queries for this use case: 

CREATE TABLE orders_enriched (
  order_id BIGINT,
  customer_id BIGINT,
  product_name STRING,
  fullname STRING,
  email STRING,
  contact STRING,
  total_price DECIMAL(10, 2)
) WITH (
  'connector' = 'kafka',
  'topic' = 'orders_enriched',
  'properties.bootstrap.servers' = '<bootstrap_servers>',
  'properties.group.id' = '<group_id>',
  'key.format' = 'json',
  'value.format' = 'json'
);

INSERT INTO orders_enriched
SELECT
  o.order_id,
  c.customer_id,
  i.product_name,
  c.fullname,
  c.email,
  c.contact,
  o.order_units * i.product_price AS total_price
FROM
  orders o
  JOIN inventory_table i ON o.product_id = i.product_id
  JOIN customers c ON o.customer_id = c.customer_id
WHERE
  o.order_status = 'confirmed';
SELECT o.order_id, o.order_date, o.order_quantity, i.inventory_quantity
FROM orders o
JOIN inventory i
ON o.product_id = i.product_id
WHERE o.order_quantity <= i.inventory_quantity;

SELECT TUMBLE_START(order_time, INTERVAL '10' MINUTE) AS window_start, COUNT(*) AS order_count
FROM orders
WHERE order_status = 'confirmed'
  AND order_time >= CURRENT_TIMESTAMP - INTERVAL '10' MINUTE
GROUP BY TUMBLE(order_time, INTERVAL '10' MINUTE)

Business impact

Reimagining customer excellence: Confluent’s real-time stream processing can enhance customer experiences by providing timely and relevant order updates throughout the order fulfillment process. Confluent serves as a single connective tissue that enables real-time interoperability of data everywhere, democratizing data across the company, which allows for a more cohesive and improved customer experience. By analyzing customer data and purchase history, these platforms can offer personalized product suggestions, cross-selling opportunities, and upselling recommendations. Redefining customer excellence not only elevates the customer experience but also boosts sales and fosters customer loyalty.

Ensured order integrity: Confluent’s Stream Governance enables a system of data streams that are reliable, discoverable, and secure, which helps screen orders within customer demographics as well as order patterns to identify discrepancies. While Stream Governance does not directly authenticate orders, it ensures the safe and proper use of business data, including order data. By maintaining order integrity, security, and compliance, Stream Governance helps prevent fraudulent activities that could lead to financial loss, ultimately leading to cost savings.

Real-time inventory replenishment: Confluent offers robust capabilities for data integration and transformation. It allows businesses to connect disparate systems and applications seamlessly, eliminating the need for manual data entry or complex integrations. This ensures accurate and consistent data across the order management, inventory management, and supply chain management system. By automating inventory tracking and providing real-time visibility into stock availability, businesses can avoid overselling or running out of popular products. From inventory management to order fulfillment, these platforms automate various processes, reducing human errors and minimizing delays. By leveraging real-time data for inventory management, businesses can reduce the risk of stockouts, overstocking, and obsolescence, which ultimately leads to significant cost savings.

Conclusion

Confluent’s real-time capabilities allow different teams within an organization to collaborate seamlessly. By providing a centralized platform for data sharing and communication, teams can work together efficiently to respond quickly to changing market demands and customer needs. 

The Order Management team, Data Engineering team, Customer Service team, Inventory Management team, Fulfillment team, Analytics team, and other teams can all work collaboratively to ensure the streamlined flow of real-time information and effective management of orders. With self-service real-time data and ease of data product discoverability, organizations can optimize their order management processes, enhance decision-making, and improve overall efficiency in delivering products and services to customers.

This enables businesses to gain a competitive edge by enabling faster response times, improving customer experiences, and staying ahead of the competition in today’s rapidly evolving marketplaces.

Here are additional resources to learn more: 

  • Mureli Srinivasa Ragavan is a Senior Solutions Engineer at Confluent with over 17 years of industry experience. Mureli is a trusted advisor to his customers, providing them with architectural strategy, technology adoption guidance, and continuous engagement model. Throughout his career, Mureli has built a reputation for his ability to create and implement innovative solutions that drive business growth and success.

    His deep understanding of cloud solutions, combined with his experience in working with Kafka and Confluent Cloud, has enabled him to build strong relationships with his clients and help them achieve their strategic goals. In his free time, Mureli enjoys exploring new technologies and fidgeting with his camera.

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