Supply Chain Logistics

Industries reliant on data, like supply chain and logistics, demand rapid and precise data processing amid continuous changes. Streamlined tracking of inventory, shipments, and order status is imperative to ensure smooth operations. Even minor disruptions in such processes can cause significant issues.

Presently, supply chain IT systems often face overload due to their fragmented, narrow-focused construction. Separate applications are crafted for distinct supply chain tasks, leading to data silos that necessitate seamless integration. For instance, an order application needs to verify inventory availability and adjust inventory levels, but this becomes complex due to separate databases.

To reconcile data, businesses often employ intricate integration technology via a services-oriented architecture (SOA). While suitable for non-real-time large data exchange, this adds unnecessary complexity and processing overhead, slowing the system and raising error risks. With rising workloads and demand for real-time responsiveness, this conventional architecture becomes a liability.

Errors in the supply chain are often due to miscalculations with data. Records might show that a shipment was delivered when in fact it was not, or that a product is available for shipment, but the inventory is actually at zero.

To minimize such errors, successful supply chain business needs an IT system that enables:

• Real-time updates to all data records in the supply chain to ensure efficient operations without costly errors
• A simplified IT architecture that ensures smooth operations as well as opportunities to innovate, without risk of breaking the system
• Faster time-to-value when developing new business applications that add innovation
• Faster data access across the many interconnected processes
• 24/7 operation with the resilience to tolerate IT system failures

Businesses should always seek adopting a more efficient integration strategy to solve challenges associated with data silos. This entails simplifying and optimizing supply chain-supporting IT operations through a hot data layer deployment. This architecture centralizes data control across silos, enabling any application to access supply chain data via the hot data layer. Updates to the layer are synchronized with the source database for consistency.

A pivotal aspect of the hot data layer is its stream processing capabilities, facilitating real-time data processing. Technologies like Apache Kafka serve as real-time messaging buses for updates, consumed by Hazelcast to update the hot data layer and streamline processes with real-time automated operations.

Logistics operations thrive on data accuracy, which hinges on real-time data delivery and sharing. Given numerous components and integration points, synchronizing data is a crucial IT system objective. Delayed information sharing between systems leads to discrepancies and inaccuracies. However, real-time updates in logistics pose several technical challenges, including:

• Trying to leverage existing (and often, legacy) systems to handle real-time transactions and real-time updates (i.e., real-time data integration)
• Efficiently upgrading away from the reliance on batch and micro-batch processes to get updated views of the operation
• Cost-effectively building and maintaining systems that immediately deliver data from one data silo to another
• Coordinating separate systems to share updates with minimal delays
• Identifying simple and repeatable ways to fix data errors in the system

IT infrastructures that run supply chain and logistics systems need real-time capabilities that ensure reliable and accurate operations. Hazelcast provides the benefits that enable systems that require efficient data management to create competitive advantage.