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The modern manufacturing and logistics landscape is driven by Industry 4.0, characterized by massive deployments of Internet of Things (IoT) sensors, real-time supply chain analytics, and edge computing architectures. While these technologies drive operational efficiency, they generate staggering amounts of telemetry data that must be ingested, processed, and stored in the cloud.
The challenge lies in the sheer volatility of this data flow. During peak production hours or high-demand shipping seasons, cloud consumption skyrockets.
While these technologies drive operational efficiency, they generate staggering amounts of telemetry data that must be ingested, processed, and stored in the cloud.
Conversely, during production lulls, maintenance windows, or off-peak hours, infrastructure often remains provisioned at peak capacity, leading to massive cloud waste.
Traditional FinOps tools fail to correlate cloud spend directly with physical production cycles, leaving logistics managers and IT directors struggling to justify the ROI of their digital transformation initiatives.
Wiv.ai bridges the gap between factory floor operations and cloud financial management, aligning cloud resources and costs with production schedules, IoT workloads, and operational demand.
By integrating with existing cloud infrastructure, Wiv.ai provides granular visibility into the exact cost of processing IoT data and running supply chain algorithms.
With its no-code automation engine, manufacturing IT teams can build dynamic workflows that scale cloud resources up or down in perfect sync with physical production schedules.
Using Wivy, teams can create workflows in plain English: “If factory line C goes offline for weekend maintenance, scale down its data processing clusters by 80%.” This helps manufacturers pay only for the cloud resources they need, turning fixed overhead into a flexible operational cost.
Automatically power down non-essential testing and analytics environments during non-working hours or factory downtime.
Create automated rules to transition massive volumes of historical sensor data from expensive hot storage to cheaper cold storage tiers after 30 days.
Link cloud scaling policies to logistics events, ensuring databases scale up automatically when large shipments are processed and scale down immediately after.
Allocate cloud costs precisely to specific manufacturing lines or distribution centers to calculate the true digital cost of producing physical goods.
Wiv.ai automatically powers down non-essential environments during factory downtime so manufacturers pay only for cloud resources supporting active operations.
Wiv.ai uses no-code workflows to scale cloud resources up or down in sync with production schedules, maintenance windows, and logistics demand.
Wiv.ai provides granular visibility into IoT processing costs and automates data lifecycle rules that move historical sensor data to lower-cost storage tiers.
See how Wiv.ai aligns cloud costs with manufacturing and logistics operations.
