Data is the critical driver of change, but even the highest quality data can't drive change in a vacuum. Simply gathering and accessing data is not enough—manufacturers must leverage it in context to make decisions that enhance value in times of digital transformation.
Why Data Contextualization is Critical During the Digital Transformation Era
Deb Geiger, VP Global Marketing | Aegis Software
One of the biggest challenges manufacturers face today is effectively managing and extracting meaningful insights from the vast amount of data available. Manufacturers collect data from multiple systems, platforms, and people, each with its unique data format, structure, and compatibility. Integrating and harmonizing data from these varied sources can be complex and time-consuming. Data challenges often arise due to differences in data models, schema, data semantics, and naming conventions.
Data is the critical driver of change, but even the highest quality data can't drive change in a vacuum. Simply gathering and accessing data is not enough—manufacturers must leverage it in context to make decisions that enhance value in times of digital transformation. Without proper context, manufacturing data can be misinterpreted or misunderstood. The lack of contextual information can lead to inaccurate analysis, resulting in flawed decision-making that adversely affects production, quality, and overall business outcomes.
By leveraging the innovative digital technologies available today that can contextualize data, manufacturers can leverage their information to make a myriad of insightful decisions, from better understanding the effects of machine upgrades to uncovering new production processes that will help optimize throughput.
This blog post will discuss important considerations for an effective data strategy, reveal why contextualization is critical, and showcase what the intelligent, agile, data-driven, and on-demand enterprise looks like.
Considerations for an Effective Data Strategy
Manufacturers must have a comprehensive data strategy, as data collection is more than just simple data entry. From the beginning, companies should focus on collecting data from various equipment types, sensors, and people on the factory floor. Contextualizing machine, human, and process data to the CAD (Computer-Aided Design) and BOM (Bill of Materials) ensures accuracy and consistency throughout the manufacturing process.
The ideal data strategy will ensure that when the data is collected, it delivers accurate analytics on objectives that have been previously established and agreed upon. For example, by setting up reports incorporating contextualized data, manufacturers can receive alerts on their key monitoring metrics, discern root causes, and enable preventative action.
Visualizing key data into bar charts, dashboards, or other display features and statistical tools allows for easier data sharing throughout the organization. For example, an end-to-end digital thread can deliver meaningful feedback to engineers and industrial design teams, enabling them to understand what is happening on the production line. The digital thread can also feed into other software applications, allowing for better integration.
Contextualization of Data is Critical
Without contextualizing data, it is very difficult to leverage any of the information being collected immediately. But manufacturers can identify bottlenecks, inefficiencies, or deviations from the optimal process flow by analyzing machine, human, and process data within the context of the design and bill of materials. This information allows data-driven decision-making to optimize resource allocation, streamline workflows, and improve productivity. To demonstrate this, let's explore a situation where data is meaningless without context.
A factory is collecting a set of raw measurements like temperature, pressure, vibration, or humidity readings from sensors installed in a machine. However, no additional information or context exists around these measurements. As a result, they don't convey much meaning on their own. While this monitoring of the machine's performance may be able to detect anomalies or deviations from normal behavior, it cannot provide any insights into the specific operation or process that the machine is involved in.
By contrast, manufacturers can contextualize this data by combining it with other relevant information, such as production schedules, maintenance logs, quality control records, operator feedback, etc. Doing so helps identify patterns, correlations, or causality relationships that can help optimize the manufacturing process, reduce downtime, improve quality, and increase efficiency.
Once contextualized data is gained, analytics are transformed into actionable insights. Contextualized data provides a core foundation for continuous improvement and innovation. By analyzing historical data linked to the CAD and BOM files, manufacturers can identify patterns, trends, and areas for enhancement. This insight can drive improvements in product design, manufacturing processes, and quality control, fostering innovation and maintaining a competitive edge in the market.
The Intelligent, Agile, Data-Driven & On-Demand Enterprise
Manufacturers are living in a state of constant change, and it's essential for the enterprise to continually make optimizations and improvements that allow it to reach the apex of its potential—to be fully intelligent, agile, data-driven, and on-demand.
With a focus on digitalization, the organization can intensify its efforts around utilizing data to drive change and success. For example, issues can sometimes arise mid-production with a sudden modification from an engineer or customer, such as a configuration or a schedule change due to a rush order. It's essential for the organization to be able to pivot as needed and address deviations as they happen with an agile and flexible manufacturing process. Data is a critical success factor in making this happen.
Additionally, manufacturers can win more business through agility by being able to easily accommodate customer requests. Like while it may take time to build new facilities or add more machinery, doing so enhances existing assets and enables agility within operations. By being able to meet and exceed fulfillment in the production line, manufacturers can ensure flexibility in the event of unplanned downtime, minimize changeover times, and accommodate small production runs. In other words, they can thrive amid today's high-quality, high-personalization customer expectations.
With systems that are optimized, flexible, and automated, "lights out" manufacturing—a production method that requires little human interaction—can be made possible. However, the reality of ever-changing demands has made finding a system that can adapt fluidly more difficult.
Bring Context to the Core of Your Data Strategy with Aegis' FactoryLogix
A robust data strategy with an emphasis on contextualization is central to digital transformation. Without a well-defined data strategy, it's all but impossible to gain the insights needed to deliver on an organization's strategic and functional objectives. Having a data strategy is essential to not only collect the appropriate data from throughout the manufacturing process and supply chain but also to clean it, then analyze and understand it.
The key to contextualization is selecting the right Industry 4.0-enabled MES that can help you realize the full value of your data. Aegis' FactoryLogix is the ideal solution to drive transformational change through improved data capabilities by providing everything needed to achieve the speed, agility, visibility, and control required by today's complex manufacturing landscape.
FactoryLogix's seamless integration and automation connects mission-critical business systems and enables insights across endless IIoT data points that goes far beyond data acquisition and insight creation—delivering the level of adaptability and contextualization needed to drive transformation.
Watch the webinar to learn more about enabling better data strategy to accelerate digitalization and innovation during the digital transformation era.
The content & opinions in this article are the author’s and do not necessarily represent the views of ManufacturingTomorrow
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