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As manufacturing and logistics companies increasingly automate workflows and deploy connected digital assets, their IT footprint becomes more dynamic and distributed. Datadog enables these firms to gain real-time insight into the health and performance of all their digital assets, bringing together data from disparate applications, physical devices, and operational platforms in a single pane of glass.
From shipment RFIDs to robotic assembly lines, manufacturing and logistics companies need to track the status, location, and performance of thousands to millions of assets. Datadog allows firms to track aggregated performance metrics for an entire fleet, facility, or product line, then drill down to inspect the health or status of an individual asset, at global scale. With Datadog's robust dashboarding and alerting features, engineering teams can pinpoint issues in individual devices or software systems and quickly correlate application and infrastructure data to determine the cause.
Logistics and manufacturing processes often rely on heterogeneous platforms, spanning on-premises and public cloud infrastructure, third-party systems, and existing systems from subsidiaries or acquisitions in an integrated supply chain. Datadog brings together data from 400 out-of-the-box integrations, including support for all leading public cloud providers, private cloud platforms, and on-premises infrastructure environments. With simple, extensible instrumentation, engineers can monitor third-party systems and proprietary or legacy technologies alongside the rest of the company's applications, devices, and IT infrastructure components—all in the same pane of glass.
As manufacturing and logistics firms increasingly deploy IoT devices, operations teams face a new set of challenges: collecting and analyzing hardware and application performance data from a huge number of geographically dispersed devices. Datadog collects, aggregates, and analyzes data from IoT fleets at any scale, so engineering teams can monitor the performance and health of IoT hardware alongside KPIs from the applications running on the devices. With built-in machine learning algorithms for forecasting, anomaly detection, and outlier detection, Datadog can automatically identify individual devices that need maintenance or predict when bottlenecks will arise.