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Turn data into intelligent operations

Connect production data with the batch, process and facility conditions behind it. Reduce repetitive work, focus technical effort on exceptions and use operating knowledge to improve performance and implement changes with less disruption.

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Make production knowledge improve performance

Each production run should add to what your organization knows. Linking measurements to their batch, process step, material and applicable process definition makes that experience useful. Contextualized, traceable data also provides a foundation for Industrial AI applications with a defined purpose. Siemens connects automation, manufacturing execution and operational information to help teams:

  • Reduce transcription and review effort
  • Support investigations and identify developing equipment problems
  • Preserve knowledge for future changes

Improve output and reduce avoidable operating work

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Reduce manual data entry and review effort

Connect process control with electronic batch records to reduce transcription. Review by exception directs attention to departures from defined requirements while people retain quality and release responsibility.

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Identify problems before disruption grows

Use process context and equipment-condition data to focus investigations and help maintenance teams recognize developing problems before they cause more costly production disruption.

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Make recipe and system changes easier to implement

Reuse established process knowledge and automation structures to reduce duplicated engineering when products, recipes or equipment change, with appropriate validation and change control.

Connect operating evidence with a defined action

Build a learning loop from production

Link each measurement to its batch, process step, material, applicable process definition and equipment, including relevant facility conditions. Compare actual performance with development assumptions to understand variation. Preserve that context as data moves between systems so operating evidence can inform process improvements and subsequent engineering.

Read how Industrial Edge connects pharma operations
Insulin Injection Pens on Conveyor Line during Production Process at Modern Pharmaceutical Factory.

Reduce transcription and focus batch review

Integrate process automation with manufacturing execution and electronic batch records to reduce manual entry. Review by exception directs attention to departures from defined requirements. More complete documentation can reduce review effort and free skilled people for investigations; authorized personnel retain quality and release decisions.

Explore electronic batch recording and review
Automated pharmaceutical conveyor line, robotic arms handling medical vials.

Define the decision that analytics will support

Process analytical technology combines measurements and models to assess quality attributes during production. Industrial AI can help investigate deviations or identify equipment problems. Distinguish recommendations from control actions. Use applications fit for their intended use with appropriate validation, and measure results such as shorter investigations or lower downtime.

Download the Quality by Design white paper
Two side by side computer screens with SIMATIC SIPAT application open (Process Analytical Technology).

Reuse knowledge when products and systems change

Connected manufacturing execution and automation can reduce duplicated subprocess modeling. Reusable structures and compatible interfaces help limit custom integration when recipes or equipment change. Preserve engineering information, assess impacts across connected systems and manage implementation through change control and validation.

Explore reusable data models and modular automation
Image simulating Modularization MTP.