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# Manufacturing Data Integration | How to Connect MES, ERP, and Shop Floor Data
[Alyssa Sarro](https://blog.minitab.com/en/blog/author/alyssa-sarro) | 10/13/2026
Topics: [Manufacturing](https://blog.minitab.com/en/blog/tag/manufacturing), [Data Integration](https://blog.minitab.com/en/blog/tag/data-integration), [Prolink](https://blog.minitab.com/en/blog/tag/prolink)
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Manufacturing data integration gives operations, quality, and business teams a shared view of what is happening in production and why. Across the plant, the data usually begins in separate and siloed places. Sensors record process conditions, control systems manage equipment, MES tracks production, quality systems hold inspection results, and ERP manages orders, materials, inventory, and costs. When those records cannot be related, teams spend time reconciling spreadsheets instead of acting on timely information.
The [ISA-95 model](https://www.isa.org/standards-and-publications/isa-standards/isa-95-standard) provides a practical map for exploring that segmentation. Its five levels organize manufacturing activities from the physical process through business planning. The model is technology-neutral, so manufacturers can use it to define system boundaries and data flows even when plants rely on different vendors, equipment generations, or levels of automation.
## Manufacturing data integration across company-wide operations
A useful data foundation starts with the physical process and adds context as information moves upward through control, operations, quality, and business systems. Integration works when that context remains intact between systems.
Capture the process and its signals
At the foundation is the physical production process. Sensors, smart devices, valves, and other equipment measure temperature, pressure, flow, vibration, speed, and other operating conditions at this level. This is where much of the [shop floor data collection](https://blog.minitab.com/en/blog/automated-data-collection?hsLang=en) begins. A measurement at this stage becomes becomes more useful when it carries a timestamp, unit of measure, asset identifier, and enough product or process context to interpret it. For example, this is where material is cut, mixed, heated, filled, assembled, or otherwise transformed during production.
Add control context
The next layer includes systems that control, monitor, and supervise production, such as programmable logic controllers (PLCs) and distributed control systems (DCSs). These systems give raw sensor values operational context by relating them to equipment states, setpoints, alarms, and production cycles. The goal is not to send every high-frequency tag directly to enterprise data systems. Instead, a well-designed connection should filter and aggregate the events, measurements, and summaries needed for operations and analysis without compromising performance or security. This is where SCADA systems come in; they help operators monitor equipment status, alarms, and process trends while making relevant shop floor data available to operations systems.
Connect production and quality improvement data
Manufacturing operations management (MOM) systems, including manufacturing execution systems (MES), connect machine activity and SCADA data with work orders, lots, recipes, labor, and work in process. Quality data from gages, laboratory systems, and quality management system (QMS) records should use the same product, batch, serial number, asset, and time references. This connection makes it possible to determine whether a process change, alarm, material lot, or machine condition preceded a defect instead of comparing disconnected reports after the fact.
Move from operations to business planning
Business planning and logistics are typically managed through enterprise resource planning (ERP) systems. ERP sends demand forecasts, production orders, material requirements, and schedules to operations, while MES returns data on actual output, material consumption, yield, and production status. Connecting manufacturing operations with business systems allows planners to work from current plant performance while giving production teams the context behind changing priorities and schedules. Defining this exchange between enterprise and manufacturing systems is a central focus of ISA-95.
## Choose the data relationships that support a decision
Legacy machines, spreadsheets, custom databases, and inconsistent naming conventions make integration challenging. The practical answer is not to copy every field into one giant repository. Start with a decision that currently requires manual reconciliation, such as tracing a quality issue, explaining downtime, or comparing planned and actual output. Then identify the few records needed across levels to answer that question.
For a quality use case, the essential chain might be work order to product to material lot to machine to process conditions to inspection result. Agree on identifiers, timestamps, units, and definitions before building the data flow. Preserve the authoritative system for each record. Data integration should connect those records and make their meaning consistent to support reliable analysis and decision-making.
## Move toward a connected factory with trusted context
A fully connected factory is difficult to achieve and maintain. Manufacturers everywhere face changing equipment, systems, and priorities, so integration is ongoing work. The most useful progress starts with reliable relationships across the production and business data needed for a specific decision. As those connections improve, manufacturers can [automate data collection](https://www.minitab.com/en-us/solutions/key-capabilities/automated-data-collection/), prepare data for analysis, monitor performance in real-time, and [apply statistical process control](https://www.minitab.com/en-us/solutions/analytics/statistical-process-control/) with less manual work. Tools such as [Minitab Connect](https://www.minitab.com/en-us/products/connect/) and [Prolink](https://www.minitab.com/en-us/products/prolink/) help teams make that progress by bringing data from multiple sources into governed, repeatable pipelines.
### Start with one decision your team needs to make faster. Let’s identify the data connections that would help. [](https://blog.minitab.com/hs/cta/wi/redirect?encryptedPayload=AVxigLLH9lH9m9rOl2wB7dYSE%2BGUjkCWo9mnbB0qJrtM%2BphxD6dxDoqdBZeHyQYgFjLenLsE%2BIj6gR3GDS%2F2hcWWXc4KqSJoTjg8OESa8152cfEX7TYSf9drErphmJLTfzH%2BE1oJQi7bwVdL1LJzhWFVRJXQlddhYE1AKCElgl1OLPJboX2Y2UiwsWiBLUQD2e5Le0BqWW%2Bd71%2Fyzjtv674Dlg7zIxMx&webInteractiveContentId=224193629743&portalId=3447555&hsLang=en)
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