The Ultimate Guide to Data Integration in Manufacturing

In manufacturing, the difference between industry leaders and those struggling to keep pace often comes down to how effectively they harness their data. Manufacturers generate enormous volumes of information across their operations—from shop floor sensors to customer orders. But most of this valuable intelligence remains trapped in disconnected systems, creating blind spots that lead to missed opportunities and costly inefficiencies. Manufacturing data integration brings disparate data together for a comprehensive, real-time view of operations that informs decisive action.

What Is Manufacturing Data Integration?

Data integration in manufacturing is the consolidation of information from diverse production, supply chain, and business management systems into a unified platform for comprehensive analysis and decision-making. It unifies data across groups and departments by connecting previously isolated data, equipping manufacturers to access, analyze, and act upon all the operational data at their command.

Done correctly, this unified approach provides a complete picture of a manufacturer’s operations from market research and product design to materials sourcing, production, and sales. The resulting visibility allows manufacturers to make confident decisions based on complete information rather than departmental fragments, thus accelerating production cycles, improving product quality and worker productivity, and paving the way for faster responses to shifts in the market. It also supports better coordination among all departments and more timely and accurate decision-making across the organization.

Key Takeaways

• When manufacturers treat their production and business data as a single, unified set, they gain vital visibility into all their operations and everything that feeds into them.
• Greater visibility equips manufacturers to improve product forecasting and make faster, more accurate decisions.
• Such data integration improves resource utilization, increases efficiencies, and streamlines production—giving the manufacturer a significant competitive advantage.

Manufacturing Data Integration Explained

Manufacturers generate data from an abundance of sources, including production equipment, shop floor sensors, ERP systems, manufacturing execution systems (MES), CRM applications, and Internet of Things (IoT) devices.

Business systems like ERP and CRM collect data on supply chains, inventory levels, sales, and customer demand and satisfaction, while manufacturing technologies like MES and IoT sensors provide data on the production process itself—down to the output and operational status of individual machines. When all this data is properly integrated—as in centralized, cleaned, and merged from these various sources—it can be analyzed to provide a single, comprehensive view of the entire manufacturing operation. This integration is essential for accurate, real-time decision-making, supporting process automation, and identifying trends or inefficiencies that might otherwise go unnoticed.

Conversely, a manufacturer whose data remains in silos will be left with only a partial and fragmented view of how its business is performing. As a result, decisions will be less accurate and take longer to make, turnaround times will be slower, and the manufacturer will find itself at a competitive disadvantage. Furthermore, good data hygiene is just as crucial as integration; manufacturers must first validate that their data is accurate, then plug any gaps, eliminate duplicates, and make sure that all information is up to date before integrating. Otherwise, the picture they get of their operations is likely to be distorted.

The Benefits of Data Integration in Manufacturing

The consolidation of business and production data into a unified system can give manufacturers a holistic view of operations that offers many benefits. These generally fall into the following four categories:

• Empowers better decisions: By integrating and then analyzing data from various sources, manufacturers can more accurately identify product trends and shifting customer preferences to improve the way they forecast demand and make market-driven decisions. They can also better coordinate the different operations that make up their production process, increasing efficiency and productivity.
• Identifies cost-saving measures: Greater insight and better coordination make it easier for a manufacturer to spot and eliminate waste and minimize downtime. A manufacturer that consolidates machine sensor data with maintenance logs and production schedules, for example, may discover that a particular machine frequently causes bottlenecks due to unscheduled downtime. The company can implement predictive maintenance, reducing unexpected breakdowns and saving thousands of dollars in repair costs and lost productivity.
• Optimizes resources: Improved forecasting and operational coordination help manufacturers to optimize inventory levels, reducing storage and material costs while avoiding downtime. Workforce scheduling can be done more accurately, improving the efficiency of labor on the factory line. This better resource utilization translates into fewer production delays and fatter profit margins.
• Improves data accuracy: When data is siloed and can’t be shared across systems and functions, each production team and business unit often ends up with different versions of the same data. This makes it difficult to know which version is accurate and can be trusted. But when a manufacturer integrates its data, this problem goes away; data remains consistent and reliable.

What Manufacturing Data Can Be Integrated?

Manufacturers collect many different types of data that can be integrated to good advantage. These include:

• Customer data: Data on customers is most frequently found in a manufacturer’s ERP and CRM systems and commonly includes contact and financial information, order history, and product preferences. It can be used together with demand data to improve product forecasting and with inventory and supply chain data to manage inventory levels and avoid production delays.
• Demand data: A manufacturer’s data about market demand for its products and services can come from both internal and external sources. Internally, ERP data on sales and inventory turnover can be combined with website analytics and CRM data on customer preferences and feedback to identify buying patterns and shifts in customer demand. Market research, social media monitoring, census, and other data from external sources can bolster understanding of the various social and economic factors likely to influence demand for the manufacturer’s products. When all this data is integrated, it can contribute to product development, resource allocation, and strategic planning.
• Production data: Data about a manufacturer’s operations and production process comes from several sources. These include MES and ERP systems, in addition to the operational data generated by individual machines, IoT devices, and shop floor sensors. In aggregate, production data is vital for understanding a manufacturer’s operational status, its efficiency, and potential points of failure. It also supports other critical functions, including order fulfillment, inventory management, and customer support. It can also combine with quality control, equipment and maintenance, and other types of operational data to track the entire manufacturing process in real time.
• Supply chain data: A subset of production data, supply chain data pertains to the movement of all materials, goods, and services from procurement to final delivery. This includes information about suppliers, production, inventory, and shipping. Sources include the manufacturer’s ERP, supplier relationship management, supply chain management (SCM), transportation management (TMS), and warehouse management (WMS) systems, which are all used to manage the company’s logistics, inventory, and supplier relationships. By integrating supply chain data with real-time production and inventory data, a manufacturer can quickly identify potential bottlenecks or delays in the delivery of critical components, enabling proactive adjustments to production schedules or sourcing strategies.
• Inventory data: This can also be considered a subset of production data. Inventory data is garnered from numerous sources, but especially from ERP, SCM, WMS, and point-of-sale systems; the data is collected and processed by the manufacturer’s inventory management system (IMS). The IMS then tracks inventory levels, purchases of finished goods, and product returns, with that data used to prevent production disruptions, stockouts, and lost sales. Inventory data can be combined with data from the ERP, CRM, MES, and other systems to generate more robust demand and production insights.
• Quality control data: Analyzing data from various stages of the production process helps limit product defects and confirm that finished products meet the manufacturer’s established quality standards. Quality control data comes from a broad range of sources, including production and machine sensors, SCM and IMS systems, manual inspections, product testing, and company audits. Together with MES and SCM data, these systems track and analyze the production process, which can promote process improvements and better product design.
• Equipment and maintenance data: Data from machine sensors, production line monitoring, quality control checks, and other sources is used to boost manufacturing efficiency, minimize downtime, and extend the lifespan of equipment. It can be shared with the MES and ERP systems to provide a more comprehensive view of the production process.
• Operational data: Operational data includes equipment and maintenance data, as well as data generated by the sensors, machines, and other systems on the factory floor. Machine performance, downtime, process control, environmental conditions, safety incidents, and energy consumption are also included. Along with identifying bottlenecks and improving the production process, operational data track order status, inventory levels, employee productivity, and compliance with safety and environmental standards. This data can also integrate with employee shift scheduling to discover which shifts consistently achieve higher throughput and fewer errors, revealing opportunities to share best practices for improved productivity.
• Workforce data: Beyond tracking employee hours, attendance, scheduling, and payroll, manufacturers can use workforce data to identify coverage gaps and determine if staff have the necessary experience, skill sets and certifications. This data is usually generated and processed by workforce management or human resource management systems, and often fed into the ERP to help with production planning and financial reporting.
• Financial data: In addition to universal business metrics—such as gross margins, operating expenses, and return on assets—financial data for manufacturers includes industry-specific benchmarks, such as cost of goods sold, inventory turnover, and overall equipment effectiveness. This data is primarily generated and analyzed by the manufacturer’s ERP or accounting systems. Financial data feeds into financial statements that provide a comprehensive view of the manufacturer’s financial performance and profitability. When combined with supply chain data—such as real-time information on inventory levels, supplier performance, and logistics costs—organizations get a view of how supply chain disruptions or inefficiencies directly affect profit margins and cash flow.

Data Integration Approaches for Manufacturers

Data integration in manufacturing means taking the different types of data described above and consolidating them within a common analytical platform. There are four main ways to do this:

• Extract, transform, load (ETL): The ETL process integrates data from multiple systems, such as ERP and MES, into a single, unified format. It involves pulling data from the sources (extract), cleaning and sorting it to make it consistent (transform), and then loading it into a central platform—usually a data warehouse. This method is very effective but also time-consuming and resource-intensive.
• Extract, load, transform (ELT): ELT is much faster than ETL. With ELT, data is first loaded into a data lake and then transformed as needed. While better suited than ETL for working with very large amounts of manufacturing data, ELT requires extensive data processing capabilities such as high-performance computing infrastructure, advanced transformation tools, and automation systems built to efficiently process, clean, and structure large volumes of raw manufacturing data within a centralized data lake environment.
• Change data capture (CDC): This method is particularly well suited to manufacturing environments that require changes to certain data types, such as inventory data, occur frequently and real-time synchronization of different systems. CDC involves detecting and logging changes in a database and then immediately updating all other systems that draw on this data. When dealing with very large amounts of data, however, CDC can overburden system performance, slow computing times, and result in more processing errors.
• Data virtualization: This approach involves creating a single, unified view of manufacturing data from various sources, but without physically moving the data. Instead, a “virtualization layer” of software is added that runs on a dedicated physical or virtual, cloud-based server. When a user queries it, the virtualization software then queries the multiple systems containing the actual the data before combining, transforming and presenting it to the user as a unified view.

This method’s biggest advantage is that it greatly simplifies the integration process—because there is none. And it can present the combined data in real or near real time. The main disadvantages are that it creates a single point of failure, such that a problem with the virtualization server affects all other systems that depend on the data; it relies on high-performance network and server demands because the data must be found, combined, and transformed while the user awaits the response; it has limited support for exceedingly complex transformations; and it generally has steep initial setup costs. Also, because it needs so much processing power to merge the data when queried, virtualization is not well suited to applications that require large volumes of data to be processed simultaneously.

Data Integration Use Cases in Manufacturing

Virtually every manufacturer can benefit from integrating its data. The following are some of the most common use cases for data integration in manufacturing:

• Predictive maintenance: By using real-time data to anticipate equipment failures before they occur, a predictive maintenance strategy can prevent equipment outages, slash downtime, and lower maintenance costs. Manufacturing data integration facilitates this by collecting the machine sensor and IoT data housed in multiple systems and interpreting it with advanced analytics and AI capabilities to deduce what equipment to adjust or repair. Predictive maintenance is one of the chief benefits of a smart manufacturing strategy.
• Defect analysis: An important part of quality control, defect analysis examines product flaws and anomalies in the manufacturing process to determine their cause and prevent them from reoccurring. Manufacturing data integration is a prerequisite for defect analysis, which relies on information from product lifecycle management, ERP, MES, quality control systems, and other sources to identify and determine the root causes of defects.
• Demand forecasting: Using data on sales, customer feedback, and online activity, demand forecasting analyzes future product demand to help manufacturers avoid overproduction. Predicting product demand and projected sales in a given amount of time is a boon to manufacturers, allowing for informed decisions about product design that align their supply chains and inventories with their expected production requirements. Demand forecasting depends on manufacturing data integration, since it employs AI and draws on the combined data from a manufacturer’s ERP, CRM, and other systems.
• Audit tracing: Also known as traceability, audit tracing involves tracking raw materials, components, and finished products through the entire manufacturing process. This documents every production input, as well as the final output, for accounting and regulatory purposes. Traceability software selects data from barcodes, RFID tags, and other tracking technologies, then merges and feeds it into the manufacturer’s ERP, MES, and SCM systems.
• Supply chain visibility: Data integration is the foundation for supply chain visibility (SCV), which lets manufacturers peer into their supply chains and spot inventory shortfalls, shipping delays, and other potential bottlenecks before they disrupt production. SCV software typically hinges on data about inventory and shipping schedules from a manufacturer’s ERP and IMS systems, along with order and supplier data from its CRM and TMS systems.
• Data syncing and collaboration: Two key advantages of integrating manufacturing data are maintaining data consistency and boosting collaboration across departments and functions. Manufacturers can accomplish these using tools designed to sync data from multiple databases, such as those included in many ERP and CRM systems. The benefits are plentiful, including greater teamwork, increased productivity, and more accurate and reliable data to guide operations.
• Energy optimization: Yet another use case for integrated manufacturing data is energy optimization to reduce waste and lower production costs. Energy management systems (EMS) that track energy usage in real time, along with regular energy audits, can help manufacturers rein in their energy consumption without compromising their production standards. EMS tools use data from shop floor sensors and MES to monitor, control, and cut back energy use across the manufacturing process.

Data Integration Challenges in Manufacturing

None of the points above imply that achieving true manufacturing data integration is an easy undertaking. The process has numerous challenges and they can be formidable:

• Data quality: One of the great truths about data processing is the cliché “garbage in, garbage out,” and this is certainly the case for manufacturers. If their original data was poor or incorrectly captured, or if it was fragmented and badly maintained, any analytics or decision-making using this data will produce flawed output.
• Data formatting: When data is formatted differently by each respective database, it must be reformatted before being used by other systems or shared by different departments. This severely limits the utility of the data and makes integrating manufacturing data far more difficult.
• Legacy systems: Older systems that predate the smart manufacturing revolution may not have been deployed with data integration in mind. The data generated by these systems is often siloed and formatted in ways that create problems when sharing with other systems.
• Security and compliance: When manufacturers succeed at integrating their data and sharing it broadly with different departments and suppliers, they may then face new security and compliance challenges. Teams must pay attention to tasks such as conducting data audits and integrating updated security controls to ensure secure and uncompromised data.

Manufacturing Data Integration Best Practices

When a manufacturer achieves successful data integration, it will be positioned for operational excellence and competitive advantage. Here are five best practices for achieving these objectives.

Review Your Existing Data Infrastructure

To avoid blind spots, false starts, and dead-ends, manufacturers should start an integration project by taking stock of data sources and the types of data involved. This confirms that all data follows company standards for formatting, users have easy access, data governance practices are being followed, and sharing occurs under updated security protocols. If issues arise in any of these areas, project leaders should address them with responsible managers.

Identify Potential Synergies Between Data Sets

Integration efforts require clearly defined goals. What are the key business objectives that the manufacturer wants to achieve? With these in mind, each data set requires examination for possible synergies, complementary or overlapping information, and opportunities for fresh insights upon merging the data. The manufacturer’s data strategy should be based on the insights and required governance policies uncovered by this exploration, along with protocols for data quality and security.

Don’t Overdo It

Once the process of data integration starts, some manufacturers may be tempted to incorporate every possible data source and every scintilla of information. But be wary of overdoing it. The goal of any integration is to make essential production and business data more accessible and more actionable for staff who need it for smarter decision-making. It is counterproductive to overwhelm them with more data than they need.

Prioritize Integration Projects Based on Their Potential Impact

As with most business initiatives, going after the lowest hanging fruit allows for easier first steps. Early success wins over doubters, and the completion of big-reward, little-risk projects instills confidence and encourages others to take the plunge. In practice, this means that manufacturers should prioritize integrations with the greatest potential impact, not just for the value they represent but also for the organizational momentum they can create.

Build Integrations to Scale

The whole point of manufacturers integrating their data is to create new opportunities for growth. That means that as they integrate, manufacturers should prepare to scale up their systems to support that growth. In general, manufacturers should build a flexible infrastructure that can adapt to future requirements without requiring extensive rework. One of the best ways to do this is to deploy cloud-based solutions, since these are designed to provide additional resources on demand.

Technologies for Manufacturing Data Integration

Comprehensive integration of manufacturing data depends on an array of technologies. These are four of the most important:

• The Internet of Things (IoT) and the Industrial Internet of Things (IIoT): An extension of the IoT, the IIoT refers to the network of smart devices, sensors, and applications that provide feedback on the production process, supply chain status, and inventory levels. A key element of smart manufacturing, the IIoT connects factory machines and systems in industrial environments to streamline operations, increase productivity, and support data-driven decision-making.
• Application program interfaces (APIs): The glue that binds different manufacturing systems together are APIs—the rules and protocols through which software applications exchange data with, and provide services to, one another. There are, however, significant differences in the number and types of APIs that different applications support. When building out their infrastructure, manufacturers should be careful to select applications and systems that support the broadest number of APIs, as this will simplify and lay a strong foundation for future data integration efforts.
• Artificial intelligence (AI) and machine learning (ML): In simplest terms, AI allows computers to recognize patterns—be they visual, verbal, or numeric—in much the same way as humans do. Widely used to predict equipment failures, automate operations, and improve production processes, such as quality control, these technologies can also enhance decision-making. AI and ML require data generated by ERP, MES, and many other systems in order to perform their tasks. In other words, while AI has become an essential element of a modern manufacturing environment, it is dependent on manufacturing data integration to provide the data necessary for effectiveness. AI can also facilitate and automate the data integration process itself.
• Enterprise resource planning (ERP): Used to integrate and streamline all aspects of a manufacturing business, ERP systems can serve as the nexus for the shop floor, the back office, field reps, and suppliers. By automating core processes, such as inventory and supply chain management, and providing real-time visibility into operational and financial performance, an ERP system gives manufacturers heightened control over their business. The ERP also plays a key role in the manufacturer’s data integration efforts by centralizing all production, marketing, and financial performance data in a unified database. This allows manufacturers to track, automate, monitor, and control their workflows in real time.

Harness the Full Power of Your Manufacturing Data With NetSuite ERP

NetSuite’s cloud-based ERP for manufacturing offers a comprehensive approach to manufacturing data integration. An all-in-one, AI-powered, cloud-based business management solution, NetSuite ERP runs a manufacturer’s accounting, inventory, supply chain, and other functions using a single, unified data model. In addition, NetSuite Analytics Warehouse can blend their operational data with information from ecommerce platforms, marketing systems, third-party applications, and other external sources of information. Together, NetSuite’s ERP and Analytics Warehouse can provide manufacturers with the operational visibility they need to make sound and timely decisions.

Robust data integration has become the sine que non of modern manufacturing. Smart manufacturing is data-driven, and no manufacturer today can remain competitive without making full and comprehensive use of its data. Automated controls, operational visibility, and evidence-based decision-making require data from numerous sources that are housed, blended, and analyzed in a unified fashion. By adhering to established best practices and adopting leading-edge technologies, such as AI and cloud-based ERP, manufacturers can achieve the level of data integration needed to optimize production and take full advantage of growth opportunities as they arise.

Data Integration in Manufacturing FAQs

How is data used in manufacturing?

Most production processes today are data-driven. Data is used for everything from predictive maintenance and supply chain and inventory management to production scheduling and product development. These functions all require data combined from numerous sources for analysis in a single, unified manner.

What are the four types of data integration methodologies?

The four types of data integration methodologies are:

1. Extract, transform, load (ETL), which involves pulling data from various sources (extract), cleaning and sorting it to validate consistency (transform), and then loading it into a data warehouse.
2. Extract, load, transform (ELT), a faster method than ETL that loads data into a data lake and then transforms it as needed. Though well suited for working with very large amounts of data, ELT is very resource-intensive.
3. Change data capture involves detecting and logging changes in a database and then immediately updating all other systems that draw on this data. This approach is particularly suitable for a manufacturing environment, where changes to certain types of data occur frequently.
4. Data virtualization creates a single, unified view of data from various sources without physically moving, replicating, or transforming the data before handling a user’s query. For manufacturers, the big advantage to this method is that it greatly simplifies the integration process and provides access to the combined data in real time. However, this process is also extremely resource-intensive.

How is manufacturing data integrated?

Data from a variety of internal and external sources is consolidated into a single format and stored in a centralized location, such as a data warehouse. The data sources may include ERP and manufacturing execution systems, Internet of Things sensors, and website analytics—as well as many others. The data is then analyzed and presented in a unified fashion.

What is an example of data integration in manufacturing?

Most manufacturers will feed as much data as they can into their ERP system. This may include data from intelligent machines and sensors on the shop floor, data on inventory levels from the inventory management system, customer data from the CRM, financial and sales data from the accounting system, and many other sources. The ERP uses this data to automate certain functions and provide real-time visibility into a manufacturer’s operational and financial performance.