Supervisory Control and Data Acquisition (SCADA)

Assembly Line

How to calculate digital transformation ROI

Date:

Author: Paxton Shantz

Topics: digital transformation, supervisory control and data acquisition, manufacturing execution system, enterprise resource planning

Organizations: Beckhoff Automation

To simplify and prioritize the digital vision, first consider how digital transformation for manufacturing integrates three key business components:

  • Supervisory control and data acquisition (SCADA), programmable logic controller (PLC) and control (machine automation)
  • Manufacturing execution system (MES), which includes: (part traceability, machine monitoring and machine management, i.e., recipes and so on)
  • Enterprise resource planning (ERP), which includes: (AP/AR, raw materials, purchase orders, inventory, scheduling and tracking).

Achieving large profitability and competitive gains requires seamless integration of three business components. However, it is important to begin at the machine automation level, then incorporate the MES and finally the ERP. The reason for following this path is based on data requirements but also because it is the easiest path for development.

Read more at Plant Engineering

MES & Machine Learning

Date:

Topics: manufacturing execution system, programmable logic controller, supervisory control and data acquisition

Organizations: Acerta

As the manufacturing sector continues to embrace digitalization, fully integrated manufacturing execution systems will become more and more useful for managing facilities. However, it is expensive for a plant to fully revamp their IT infrastructure. Manufacturers with partially integrated or non-existent MES won’t upgrade unless there are benefits that outweigh the costs, and returns that can be realized.

Incorporating a MES and subsequent machine learning platform into a facility’s or organization’s infrastructure reduces the cost of manual data processing. Tasks that have traditionally taken hours of manual labor, such as aggregating line data to identify trends, can be automated and completed in minutes or less. In this case, machine learning isn’t competing with statistical process control (SPC) or other traditional quality methods; it’s augmenting them so that engineers spend less time to get better insights into their operations.

Read more at Acerta Blog