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The manufacturing industry is undergoing a huge transformation. Intelligent technologies such as robotics, sensors, IoT and digital twins, key to Industry 4.0, are being implemented in manufacturing plants, especially in enormous corporations, to transition to data-driven operations that are highly capable, sustainable and responsive to changing market demands. As production scales, these intelligent factories generate massive amounts of data through connected digital systems and sensors. This data can be used by plant and operations managers to optimize factory operations and take precautions to prevent failures such as equipment failures or worker safety issues. Also to augment customer engagement.
Despite the obvious advantages, research shows that American manufacturers are losing out over $50 billion annually due to unplanned downtime. About 70% of equipment failures follow predictable patterns that can be identified and prevented. This shows that many manufacturers continue to operate time-based maintenance strategies (quarterly, semi-annual or annual assessments). However, this technique is not practical in terms of reducing operating costs. Instead, it ends up inflating it.
Moreover, the data generated is often unstructured and fragmented across legacy systems, sensors, MES, SCADA and ERP platforms. Many manufacturers lack the scale, data infrastructure and expertise to turn raw information into insights. This is where data engineering services come in, transforming scattered information across machines and production line systems into meaningful insights that aid teams become more capable and competitive without increasing overhead costs.
Development of data-driven production:
Current manufacturing plants are overflowing with data due to the introduction of industrial automation. Companies are increasingly integrating Internet of Things (IoT) sensors, robots and CNC machine tools to speed up production. This is why global industrial automation marketvalued at USD 205.86 billion in 2022, it is expected to reach USD 395.09 billion by 2029, representing a CAGR of 9.8%. These tools, along with existing ERP platforms and quality management tools, generate huge streams of information that can be used to improve productivity, reduce maintenance costs and augment sales.
But how? This is where data engineering services come into play. It is the practice of designing and building systems for aggregating, storing and analyzing data on a enormous scale. It can enable manufacturers to gain real-time insights from substantial data and make more effective, informed decisions. It is data engineers who transform expansive amounts of data into valuable strategic insights.
UptakeThe Chicago-based technology company uses data engineering techniques to analyze and predict hardware failures in advance. This helps manufacturers optimize their asset maintenance strategy (seamlessly moving from time-based to predictive and condition-based) for maximum efficiency.
What are data engineering services?
Data comes from a variety of sources: social media, emails, customer service calls, chat transcripts, IIoT sensors, manufacturing execution systems (MES), and legacy tools. These huge datasets, while very useful, are rarely used to their full potential. They sit in silos or fragmented systems. Additionally, the mechanism required to transform and analyze this data is broken or missing. Without real-time, actionable insights, it can be very tough to stay competitive in a rapidly evolving industrial environment. This is exactly what data engineering services do. It involves the design, development and management of data pipelines, infrastructure and architecture to make enterprise data useful.
For manufacturers, this usually means:
- Integration of data from various sources and media
- Cleaning and transforming raw, inconsistent, unstructured and semi-structured data into standardized, readable formats.
- Create scalable data pipelines that support both real-time streaming and batch data.
- Implementing data lakes or warehouses for secure storage and capable querying.
So that production teams have actionable data at their fingertips. Michael Hausenblassolution engineering manager on the AWS open source observability services team, defines its meaning:
“Data engineering is the bridge that connects broad business goals with detailed technical implementation.”
Data engineering in action:
Step 1: Data acquisition: Moving data from sources (databases, files and websites) to a cloud storage platform, data warehouse/data lake. This process may involve real-time transfers or straightforward batch transfers.
Data warehouse and data lake:
A data lake stores massive amounts of raw, unstructured data (images, audio, video, and meeting notes) as well as structured data, while a data warehouse only stores structured data for business analysis and reporting.
- Data warehouse platforms: Amazon Redshift, Google BigQuery and Snowflake.
- Data lake platforms: Amazon Lake Formation, Apache Iceberg Lakehouse, and Azure Data Lake Storage.
Step 2: Data storage: The captured data is then stored in a central database for further processing and evaluation. It allows users to access and manage files from anywhere, on any device, all they need is an Internet connection.
Step 3: Data integration: Break down the data silo and maintain a consistent, right and up-to-date view across systems for a comprehensive, unified view. It provides the foundation for business intelligence and advanced analytics, helping teams make more informed decisions that can augment productivity and customer engagement.
Step 4: Data Processing: Data from warehouses/lakes is extracted, categorized, cleansed and formatted, making the raw, unstructured data suitable for analysis.
Step 5: Data visualization: Presenting intricate data in visually appealing, easy-to-understand formats so you can make more informed decisions. Tableau, Microsoft Power BI, and Zoho are some of the data visualization tools that also offer artificial intelligence capabilities.
These insights can aid manufacturers identify up-to-date opportunities, streamline operations, improve profitability and reach up-to-date heights. Get more observations here.
Why manufacturing needs data engineering now more than ever
Industrial IoT (IIoT) data explosion.
Traditionally, methods such as assembly lines, casting and machining were used, with operators and managers recording data using manual logs, supervisory control and data acquisition (SCADA) systems, ERP systems, quality control systems and plant equipment records. Maintenance was time-based, not proactive or status-based.
Therefore, equipment failures and factory downtime were commonplace.
The emergence of intelligent factories, which operate connected systems, machines and devices to collect, share and analyze data in real time, has truly transformed manufacturing processes. A single production line can generate terabytes of data per day, such as temperature readings, vibration data, and defect counts. Managing this flood of information and optimizing maintenance processes requires a resilient data architecture. Teams of data engineers build pipelines connecting factory machines, sensors, and manufacturing systems to collect real-time data from the production line, monitor product quality, and track supply chain data, enabling predictive maintenance and immediate alerts when problems arise. Did you know that according to US Department of EnergyPreventive maintenance can deliver up to 18% savings compared to reactive maintenance?
Connecting legacy systems to contemporary platforms:
Legacy systems don’t integrate easily with contemporary cloud or artificial intelligence platforms. However, discarding them or replacing the plant’s architectural heritage can be time-consuming and high-priced. Data engineering services enable seamless integration via APIs and ETL tools, connecting legacy and up-to-date systems. Additionally, AI agents can be used as sidecars or adapters to provide teams with real-time insight. This interoperability is crucial for end-to-end operational visibility.
Improving supply chain and inventory management:
Acquisition. Logistics. Production. The supply chain can be very intricate. Data engineering helps integrate this data to provide a unified view that can optimize inventory levels, predict delays and shortages, and enable capable decision-making. For example, if a plant manager gets real-time visibility on a monitor, it means next week’s production could be delayed due to logistical challenges. The team can then take proactive steps to address the issue so that customer (buyer) relationships are not strained.
Application
From optimizing manufacturing processes (collecting, integrating and analyzing data from multiple sources) to improving product design (collecting and processing feedback from customers, suppliers and partners), enabling predictive maintenance and helping to create up-to-date business models, data engineering services open up untapped opportunities for manufacturing enterprises. As more companies continue to transition to intelligent manufacturing by implementing advanced, highly integrated technologies in manufacturing, the need for data engineering will continue to evolve. It can play a decisive role in shaping the digital future and staying competitive.
By transforming raw data into actionable information, these services enable manufacturers to reduce operational downtime, optimize production and gain a competitive advantage in an increasingly connected world. The choice is yours: are you ready to take full advantage of this untapped data mine?