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Smart Warehouse
IoT and robotics are revolutionizing the traditional warehouse to meet same-day (and same-hour) delivery expectations. Use Confluent’s data streaming platform to bring real time to your smart warehouse, increasing automation, efficiency, and cost savings.
Unleash the Power of IoT
From live dashboards, IoT sensors monitoring humidity and temperature, robots autonomously retrieving items off of shelves to tracking devices on pallets – real-time data is crucial for smart warehouse operations. Confluent’s data streaming platform brings together disparate IoT data, inventory and order data, warehouse infrastructure data to facilitate real-time data processing, analysis, and decision-making.
Without the need to manage data infrastructure, development teams can focus on building new features such as back-in-stock customer notifications. Additionally, real-time data can be used for artificial intelligence (AI) and machine learning (ML) use cases such as predictive analytics or building a GenAI chatbot for warehouse operators.
Enable robot order picking for greater efficiency and accuracy.
Save time and boost productivity for operators by providing up-to-the-second data.
Leverage machine learning for optimizing FIFO (first in, first out), shelf stocking, forklift routes, etc.
Build with Confluent
This use case leverages the following building blocks in Confluent Cloud.
Reference Architecture
Leverage Confluent’s pre-built connectors to ingest inventory data from relational databases such as SQL Server and PostgreSQL as well as telemetry data from custom producers, IoT sensors and equipment. Cluster linking connects on-prem and cloud clusters, mirroring topics so that data can be seamlessly shared.
Use Flink stream processing to join and enrich data streams, creating data products such as robot battery alerts or inventory count. These data products can be shared with downstream systems such as apps, Google BigQuery to create dashboards, and to train ML models.
Stream Governance ensures data quality, security and compliance, allowing teams to reliably detect sensor/equipment anomalies, fraud, and other real-time events.