Voss Automotive Boosts Manufacturing Processes with DataOps

In the automotive market, where efficiency is key to competitiveness, Voss Automotive has recognized the power of data to transform its manufacturing processes. The company has launched a strong DataOps plan that focuses on easy data access, consistent data formats and uniform KPI (key performance indicator) metrics. By enabling near real-time data visualization, Voss not only makes sense of its data with context-rich insights, but also establishes a strong data foundation that enables diverse use-cases at scale more efficiently.

Taming complex industrial data

Voss faced the challenge of managing and using valuable data from a heterogeneous machine park, with data formats ranging from OPC UA to simple text files. Each machine produced a continuous flow of information that was underutilized due to lack of contextual insight and uniformity. Like many manufacturing companies, Voss’s efforts to better manage its data were impacted by the reality of the company’s diverse, historically grown production systems that couldn’t be ripped out and replaced immediately.

Having tested different approaches to digital transformation, Voss knew it wanted to combine extensive edge processing with cloud analytics for scalability and cost optimization. This led Voss to look for a robust DataOps platform that could seamlessly connect devices, standardize and model data for comprehension, carry out analytics at the edge and easily connect to their cloud infrastructure. They also wanted to ensure the platform could support automated deployment and configuration processes to optimize operational efficiency.

A comprehensive data strategy

Voss turned to Litmus to revolutionize not only how the company captures, processes and leverages its operations data, but also how they manage the data infrastructure layer underneath.

The result is a comprehensive industrial DataOps strategy that encompasses:

Device connectivity: Seamless connection to old and new production equipment alike for comprehensive data capture.
Context-rich data modeling: Enrichment of machine data with context through metadata definitions stored in Litmus Edge’ Device Hub. UNS (universal namespace) naming conventions ensure standardized topic streaming for consistent data representation.
Automated KPI calculations and visualization: Extensive automation to calculate and standardize various operational KPIs across facilities, such as machine health, energy consumption or OEE.
Data integration: Bi-directional MQTT connections enable seamless data exchange between OT and IT systems. The outbound connection allows real-time, change-based data transfer to minimize data noise while the inbound connection from systems such as ERP provides business critical data for additional KPIs.
Scalability: Establishment of a flexible yet standardized data infrastructure that can be readily extended to encompass additional production facilities.

Voss Quick Connect System 246NX is the VOSS standard for the connection of plastic tubes in fuel, SCR and cooling systems.

The adoption of these strategies allowed Voss to not only streamline its data processes but bring a new level of intelligence and efficiency into its manufacturing operations. The implementation began in December 2023 and took about three months to fully implement.

“Using Litmus Edge, we've improved the integration and automation of our OT infrastructure management and deployment. This advancement helps us move towards unifying and standardizing our data practices throughout our organization,” said Sacha Dannewitz, chief information officer at Voss Automotive.

A manufacturing intelligence transformation

In Litmus Edge, every data point collected is analyzed and processed before the data is sent to an external app, tool or cloud-based services for advanced applications. As the data gets processed, each data point gets context added to it, providing the what, when, how, why and who owns the data.

To make it simpler for data teams, Litmus Edge has more than 50 built-in statistical functions including anomaly detection, multiple filters and averages that can analyze the data and immediately generate common KPIs in near real-time. The generated context can be correlated to derive additional insights like energy usage from machine data, providing data driven energy monitoring and control support, without the need for an expensive energy monitoring software. This eliminates the need for complex integrations, latency and additional data transfer challenges.

By adding context to its data, Voss was able to gain actionable insights that had previously been obscured by the sheer volume and complexity of information. The automation of KPIs brought standardization and efficiency, allowing for a more nuanced understanding of machine performance and energy consumption. Furthermore, the change of value feature in Litmus Edge ensured that only the most critical and relevant data were communicated, preserving bandwidth and focusing attention where it was needed most.

Sebastian Rodtmann, digital manufacturing business analyst at Voss Automotive, noted that “with Litmus Edge we have been able to add context to manufacturing data right at the source, calculate the desired KPIs at the edge and send them to the cloud—all fully automated within one workflow and one software piece. Thereby, we are replacing four steps with one, resulting in a more efficient, robust and agile data landscape.”

John Younes is chief operations officer and co-founder of Litmus.