Voss Automotive has embarked on a transformative journey to enhance its manufacturing processes by developing and implementing a meticulous DataOps strategy. Recognizing the paramount importance of efficient data management in maintaining a competitive edge in the automotive industry, Voss Automotive has leveraged the power of data to revolutionize its operations. The company’s foray into DataOps is characterized by its focus on seamless data access, consistency in data formats, and standardized KPI (key performance indicator) metrics. These efforts have culminated in near real-time data visualization capabilities that provide context-rich insights, augmenting data interpretation and facilitating a robust data foundation capable of supporting diverse use cases at scale more efficiently.
One of the significant challenges Voss faced was the management of complex industrial data arising from a heterogeneous machine park. The data formats ranged from OPC UA to basic text files, and there was a continuous flow of valuable information from each machine that historically remained underutilized due to a lack of contextual insight and uniformity. This challenge was further compounded by the diverse and historically developed production systems that could not be immediately replaced. For Voss, addressing these challenges was critical to realizing the full potential of their data and achieving greater operational efficiency.
The Need for a Robust DataOps Strategy
To navigate these complexities, Voss Automotive embraced a comprehensive DataOps approach, focusing on the need for seamless data access and standardized KPIs. Voss recognized that only through a consistent and organized data management system could it achieve near real-time visualization and insights critical for operational excellence. By ensuring that data formats were consistent across all machines, Voss aimed to create a standardized data environment that would enable more accurate and meaningful interpretations.
The decision to integrate such a strategy wasn’t taken lightly. Voss had to consider the breadth and depth of their industrial data sources, which varied significantly in format and complexity. Moreover, the challenge of harnessing this data was exacerbated by the aging and varied infrastructure where these machines operated. Voss needed a robust solution that could seamlessly integrate with their diverse array of equipment, ensuring that data from all sources could be captured comprehensively and utilized effectively. A meticulous strategy was required to not only capture and process this data but also to standardize it in a way that it could be analyzed and acted upon in real-time.
Leveraging Edge Processing and Cloud Analytics
Voss’s earlier digital transformation efforts underscored the necessity of combining edge processing with cloud analytics for scalability and cost optimization. This led Voss to seek a DataOps platform that could seamlessly connect devices, enable data standardization and modeling, perform edge analytics, and facilitate integration with their cloud infrastructure. Moreover, Voss required the platform to support automated deployment and configuration processes to enhance operational efficiency. To fulfill these requirements, Voss turned to Litmus, a comprehensive DataOps platform.
Litmus revolutionized Voss’s approach to capturing, processing, and leveraging operational data, as well as managing the underlying data infrastructure layer. The result was a multifaceted industrial DataOps strategy composed of several critical components, leveraging edge processing and cloud analytics to provide a scalable solution for their data management needs. By combining these advanced technologies, Voss was able to address both the volume and complexity of their data, ensuring that they could extract meaningful insights and optimize their operations accordingly.
Enhancing Device Connectivity and Data Enrichment
A key component of the DataOps strategy was enhancing device connectivity, facilitated through seamless integration with both old and new production equipment to ensure comprehensive data capture. Additionally, to enrich the machine data, Litmus Edge utilized metadata definitions housed in its Device Hub, implementing UNS (universal namespace) naming conventions for consistent topic streaming. This standardized data representation was essential for creating a uniform data environment across Voss’s production facilities.
Automation also played a substantial role in this transformation. By automating KPI calculations and standardizing operational metrics such as machine health, energy consumption, and OEE (Overall Equipment Efficiency), Litmus Edge enabled Voss to dramatically improve operational efficiency. This automated approach not only reduced manual intervention but also ensured accuracy and consistency in data interpretation, thereby providing a clearer picture of operational performance across the board.
Seamless Data Integration and Scalability
Through enhanced automation and bi-directional MQTT connections, Voss was able to integrate data seamlessly between OT (operational technology) and IT (information technology) systems. This integration facilitated real-time, change-based data transfer, minimizing data noise and ensuring that only relevant information was communicated. Inbound connections from ERP systems further enriched the data with business-critical insights, contributing to more robust KPI calculations and operational intelligence.
Another significant achievement was the establishment of a flexible yet standardized data infrastructure, enabling scalability and easy extension to additional production facilities. The strategies implemented effectively streamlined Voss’s data processes, introducing a new level of efficiency and intelligence into their manufacturing operations. Initiated in December 2023, the three-month implementation period culminated in a more agile and data-driven manufacturing environment that was poised for future growth and innovation.
Real-Time Data Analysis and Contextual Insights
An essential feature of Litmus Edge is its capability to analyze and process every data point before transmitting it to external applications, tools, or cloud-based services for advanced applications. This process involves adding vital context to each data point, detailing the who, what, when, how, and why associated with the data. By simplifying data analysis, Litmus Edge empowered Voss’s teams with over 50 built-in statistical functions, including anomaly detection, multiple filters, and averages, generating KPIs in near real-time. This approach provided immediate insights without the complications of complex integrations, latency, or additional data transfer challenges.
Moreover, by contextualizing data, Voss Automotive garnered actionable insights that were previously obscured by the sheer volume and intricacy of information. Automated KPI calculations standardized processes and enhanced efficiency, leading to a more nuanced comprehension of machine performance and energy consumption. Litmus Edge’s change-of-value feature ensured that only critical and relevant data were communicated, preserving bandwidth and focusing attention on essential aspects, ultimately leading to more informed decision-making and improved operational outcomes.
Streamlined Data Processes and Operational Intelligence
Voss Automotive is undergoing a transformative shift to enhance its manufacturing processes by developing and implementing a comprehensive DataOps strategy. Recognizing the critical importance of effective data management to stay competitive in the automotive industry, Voss Automotive is leveraging data to revolutionize its operations. This DataOps initiative focuses on ensuring seamless data access, consistency in data formats, and standardized key performance indicators (KPIs). These efforts have led to near real-time data visualization capabilities that provide context-rich insights, improving data interpretation and creating a strong data foundation that supports diverse use cases at scale more efficiently.
One major challenge Voss faced was managing complex industrial data from a heterogeneous array of machines. The data formats varied from OPC UA to basic text files, with a continuous flow of valuable information from each machine historically going underutilized due to a lack of contextual insight and uniformity. This challenge was intensified by the diverse and historically developed production systems that couldn’t be easily replaced. Addressing these challenges was crucial for Voss to unlock the full potential of their data and achieve greater operational efficiency.
