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Gain performance visibility into all digital assets around the globe
As manufacturing and logistics companies increasingly automate workflows and deploy digitally connected assets, they also introduce new, dynamic infrastructure that must be actively managed. Ensuring that a globally distributed device fleet or a robotic assembly line is consistently performing as expected can be challenging. The same technologies that allow for instant scalability, immediate global coverage, and improved efficiency also make operational backends vastly more complex and prone to unforeseen issues.
Manufacturing and logistics firms that use Datadog are able to gain real-time insight and control over the health of all their digital assets. Datadog provides visibility and analytics into application performance, device hardware metrics, and usage data generated by these assets. These insights provide the capabilities to not only assure that processes are executing as expected, but also to pinpoint issues in internal or third-party systems to allow for immediate remediation.
Manufacturing and logistics companies often track the performance of thousands to millions of assets in real time, ranging from shipment RFIDs to actions undertaken by robotic assembly workers. This represents an unfathomably large data set, making it difficult to know when an object, somewhere in the world, is experiencing performance issues. Datadog allows manufacturing and logistics firms to track the health of each individual asset in their employ, even at the scale of hundreds of millions of assets. Engineers can use Datadog's robust dashboarding and alerting features to pinpoint issues in individual devices and quickly synthesize related application and infrastructure data to spot a problem’s underlying source.
Nearly every process run by a logistics or manufacturing company involves a number of heterogeneous platforms. These platforms may include on-premise and public cloud–hosted infrastructure, external systems operated by third parties, or new acquisitions in an integrated supply chain. Yet an issue in any platform, whether in-house or external, in the cloud or on-premise, can lead to performance bottlenecks. Datadog allows engineers to have “eyes” on all systems involved in a process. Datadog can take in data from any third-party systems, plus 350 out-of-the-box integrations, which include support for all leading public cloud providers as well as systems commonly used with on-premise hardware. All of this data—sourced from any application, device, or IT infrastructure component, regardless of owning party—can then be jointly combined, correlated, and analyzed in the same pane of glass.
As manufacturing and logistics firms increasingly deploy IoT devices, operations teams face an additional set of challenges: collecting and analyzing hardware performance data for a geographically disparate fleet of devices, and then correlating these metrics with the performance of the software running on the devices. Datadog offers specialized handling for IoT devices, as well as the ability to correlate the performance data of IoT hardware alongside the KPIs of the applications operating on the device. Additionally, many of Datadog’s machine learning–based forecasting and anomaly detection algorithms can predict and visualize resource usage for these devices. Firms using Datadog can thus identify early trends that can indicate future maintenance issues.