Data engineering refers to the design of systems that are capable of collecting, analyzing, and storing data at a large scale. In manufacturing, data engineering aids in optimizing operations and enhancing productivity while ensuring curated data that is both compliant and high in integrity.
The increased efficiency in data “wrangling” means that more accurate modeling and planning may be done, enabling manufacturers to make stronger data-driven decisions. More recently, real-time monitoring of manufacturing processes has been identified as an area associated with significant opportunities.
Through the application of best practice data engineering, implementations of real-time monitoring have achieved a measurable reduction in downtime while maintaining excellent quality control of the processed data.
By leveraging the benefits available through data engineering, manufacturers have enhanced their efficiency, increased productivity, and improved decision-making capabilities. Data engineering plays a pivotal role in modern manufacturing processes and is indispensable for many reasons, some of which we will discuss here.
In this blog, we will examine the opportunities, challenges, and possible future trends driven by the application of data engineering in the manufacturing industry.
Use Cases of Data Engineering in Manufacturing
Predictive Maintenance
Benefit:
Manufacturers can leverage data engineering to construct maintenance strategies based on predictive data modeling that helps detect anomalies in advance. These anomalies are often related to equipment failure and must be detected beforehand to avoid last-minute mishaps. This is called sensor-based condition monitoring.
Example:
Uptake, a Chicago-based technology company, leverages data engineering techniques to understand and predict equipment failure in advance. It also uses data engineering principles to optimize maintenance schedules for maximum efficiency.
Quality Control
Benefit:
Data engineering enables manufacturers to construct quality control systems in real-time. The aim is to ensure that a high-quality standard is kept, meeting or often exceeding regulatory compliance guidelines.
Data engineering practices aid in identifying potential issues and defects by analyzing data from production processes, enabling opportunities for appropriate corrective actions.
Example:
Siemens’s popular Manufacturing Operations Management (MOM) software uses data engineering techniques for real-time monitoring and quality control. It ensures the processes meet quality compliance requirements and improves operational efficiency.
Process Optimization
Benefit:
Manufacturers are able to utilize data engineering techniques to streamline their processes and drive further efficiency. By analyzing data across all stages of production, these operations will experience enhanced optimization of processes while simultaneously increasing output and reducing downtime.
They will have an edge in understanding seasonal and market changes better and be able to plan and prepare for them accordingly.
Example:
Companies such as Rockwell Automation use data engineering practices to streamline and optimize production methodologies by leveraging detailed analysis to identify the most suitable focus areas. This results in a concentrated approach that directs efforts toward the most significant improvements in efficiency to achieve the desired output levels.
Challenges
Data Quality
While data engineering is leveraged for quality compliance and assurance purposes, data accuracy remains one of the key areas posing a significant challenge and threat. A complete data set can easily lead to better-informed decision-making and correct conclusions, causing huge blows to manufacturers.
Without proper data versioning and process control, one might make decisions based on outdated or stale data. Creating and maintaining a sponsored and supported Single Source of Truth becomes more critical, which would be referenced for all up-to-date company needs.
Security Threats
Safeguarding large amounts of data has been an overlooked challenge for many established manufacturing firms. With the enormous amount of data involved, ensuring compliance with security measures has been an epic problem, where the penalty for non-compliance has only increased (and continues to do so) with time.
Hence, you need to ensure your data is stored securely and compliant with all relevant data legislation.
Processing High-Volume Data
A manufacturer that leverages data engineering for streamlining processes will – without a doubt – generate an enormous amount of data. Handling this volume of data (in the time required) is a challenge that stresses the computing power of more “traditional” technology ecosystems and can require specialized processing.
In a world where leveraging all available data grants a competitive edge, effectively managing and handling the sheer volume of information can be daunting.
Opportunities
Cost Reduction
One of the most significant and obvious benefits when manufacturers leverage data engineering, is a reduction in operating costs. The increased efficiency of evaluating operational data also leads directly to lower costs associated with process streamlining, increasing operational process efficiency, and quality checks.
Enhanced Quality
Since data engineering is used for many verification and validation quality checks, its implementation has reduced processing waste (increasing net yield). It has been effective at identifying and alerting anomalies with advance warning. This has helped manufacturers take timely actions without compromising on quality.
Increased Efficiency
Data engineering has successfully streamlined manufacturing-related processes, reduced related costs, and monitored processes in real time. The net result is enhanced processing efficiency, reduced scrap/waste, and a well-balanced effort required for acceptable (or higher) throughput levels.
Future of Data Engineering in Manufacturing
Edge Computing
With an industry focus on improving real-time monitoring, manufacturing is uniquely positioned to introduce and leverage the advantages of edge computing. Edge computing refers to a paradigm of placing computing resources and systems in close proximity to the point of data interaction.
This helps bring data storage and its computation closer to the sources from which data is obtained and is credited with improving response time and reducing latency.
Integration of Artificial Intelligence and Machine Learning
Artificial Intelligence (AI) and Machine Learning (ML) have the potential to improve all aspects of data engineering and make it more accessible and beneficial for the manufacturing industry, which is dependent mainly on efficient data processing.
Data-driven decision-making, defect detection, and predictive analysis become more accurate and effective with the proper integration of AI and ML in data engineering models.
Working Toward a More Sustainable Earth
Data engineering has had lasting impacts on waste reduction, proper resource allocation, and optimization of processes. All of these play a hand in conserving resources (labor, materials, energy), which in turn reduces the burden on the environment and works toward creating a healthier and more sustainable ecosystem.
Sample Architecture with phData
The following case study involves a scenario where data engineering was utilized to help a manufacturing organization improve its internal processes and drive business value. While the original use case involved customer interaction, additional value was quickly identified through the need for a centralized data repository and an architecture to support more involved analytics projects in the future.
You can find more details about this case study here.
Closing
Data engineering practices applied to manufacturing have been a boon to the industry, creating near-limitless opportunities for developing true continuous improvement capability.
Companies leveraging these techniques have been able to manage an enormous amount of data, enabling them to streamline processes, conduct periodic (and real-time) quality checks, predict anomalies in advance, and reduce total scrap and other waste.
The perpetual advancements in data engineering and data science continue to identify additional novel applications within the manufacturing industry. As research and advancements continue, the list of use cases for potential applications will grow while also increasing the accessibility of achieving previously unknown levels of operational efficiency.
Edge computing and the integration of AI and ML into manufacturing data models will give rise to a more sustainable approach overall. As the manufacturing landscape evolves, those better positioned to leverage all available data for informed decision-making will stand out as industry leaders.
Want To Learn How Data Science Can Help Your Organization Stand Out As An Industry Leader?
FAQs
Data science aids the manufacturing industry in predictive and preventive maintenance, quality control, and process optimization.
Data in the manufacturing industry helps enhance quality control measures, operational efficiency, and inventory management. This results in reduced costs and less waste.
