Manufacturers need pragmatic guidance when choosing network protocols that must balance responsiveness, high data throughput, and long-term maintainability. This paper presents a step-by-step, criteria-driven framework that scores protocols on six practical dimensions, real-time behavior, bandwidth, interoperability, security, IIoT readiness, and legacy support and demonstrates the approach on both greenfield and brownfield scenarios. By combining vendor specifications, peer-reviewed studies, and field experience, the framework delivers transparent, weighted rankings designed to help engineers make defensible deployment choices.
This paper explores how network protocols can be mapped to different layers of the automation pyramid, ranging from field-level communication to enterprise-level. For example, Profinet is shown to be highly effective for time-critical applications such as robotic assembly and motion control due to its deterministic, real-time ethernet capabilities. Meanwhile, IO-Link offers a point-to-point solution with its diagnostic capabilities for intelligent sensors and actuators, making it ideal for IIoT-driven data collection. Other protocols, such as EtherCAT and OPC UA, each provide unique advantages in speed, interoperability, or security, underscoring the importance of matching protocol features with specific factory needs. The paper identifies six critical factors for protocol selection.
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Real-Time Requirements: Deterministic behaviour for high-speed control loops
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Scalability & Integration: Seamless integration with existing and future systems
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Security & Reliability: Protecting critical operations from unauthorized access or failure
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Cost & Complexity: Balancing performance benefits with implementation overhead
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IIoT Readiness: Enabling cloud connectivity, data analytics, and remote monitoring
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Legacy Support: Availability of gateways, backward compatibility, and incremental modernization.
By presenting a clear, repeatable process for protocol selection, this framework enables engineers and decision-makers to make data-driven decisions that align with immediate operational requirements and long-term digitalization objectives.