Case study · Packaging machine builder

1,500+ machines connected.
~70% of service issues resolved remotely.
One data model across the entire fleet.

TMI builds and delivers packaging machines worldwide. With StriData, they turned raw IXON data into a structured analytics layer used by service teams and customers across 40+ countries — without rebuilding dashboards per site.

The company

TMI is a packaging machine builder operating globally, delivering machines to customers across a wide range of production environments. Their installed base spans multiple countries, customers, and machine types — each generating large volumes of operational data.

Like many OEMs using IXON, TMI had connectivity in place. Machine data was available, but not structured in a way that could be used consistently across their service team or shared with customers at scale.

1,500+
machines deployed across
40+ countries worldwide

The challenge

TMI had connectivity in place through IXON. Machine data was available — including states, alarms, and production counters — but it was not structured in a way that could be used consistently across their organization.

Service teams worked reactively. Engineers would log into individual machines to investigate issues as they occurred, without a clear overview of patterns across machines, customers, or time. Every analysis started from scratch.

At the same time, customers increasingly expected access to their own machine performance. Delivering this required building dashboards per customer — an approach that quickly becomes unmanageable as the installed base grows.

What StriData did

Instead of building dashboards per customer, StriData introduced a structured data layer for TMI’s machine types — making it possible to deploy analytics consistently across the entire installed base.

01

Connect and extract machine data

StriData connects directly to TMI’s IXON environment and extracts machine states, alarms, and production counters through the API — without changes to the existing setup.

02

Build a standardized data model

Machine data is structured into a reusable model with consistent KPI definitions for each machine type. This model is built once and forms the foundation for all analytics.

03

Apply across the entire fleet

The same data model is applied to every connected machine and customer site. New deployments inherit the structure instantly — no rebuilding required per customer.

The result

A scalable analytics layer across TMI’s installed base

With one standardized data model in place, TMI moved beyond one-off troubleshooting and customer-specific reporting toward a repeatable analytics setup that scales across sites, machines, and customers.

~70%
of service issues resolved remotely
5 min
to onboard a new customer site to the analytics layer
1,500+
machines connected under one standardized model

The result is not just faster reporting. TMI now has a structured way to analyze machine performance across its fleet, support customers with direct access to their data, and expand remote service without rebuilding the setup for every new deployment.

Dashboard example

What this looks like in practice

TMI’s Power BI environment gives service teams and customers direct visibility into machine performance, downtime, and OEE across connected sites.

TMI Power BI dashboard example showing machine performance and OEE monitoring
Example of TMI’s Power BI dashboard, using structured IXON machine data to monitor OEE, machine status, and performance across customer environments.
Customer feedback

What TMI says about working with StriData

“We are very grateful for the partnership with StriData and their willingness to ensure the customer's needs. They offer excellent service, always with a good attitude.”

Oriol Miarnau
Automation Engineer, TMI

Want to see what this could look like for your machines?

The Quick Scan is the best starting point if you already have machine connectivity in place and want to understand what a scalable reporting or service setup could look like for your installed base.

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