SA Concepts

Design principles that strengthen Situation Awareness in industrial dashboards — helping people perceive, comprehend, and project the state of their systems with greater clarity.

Why SA Concepts Matter

Each concept addresses a real design challenge faced by operators, engineers, and teams as they monitor and respond to process information. Poor visibility, overload, and lagging data often lead to delayed or incorrect decisions — gaps in Situation Awareness that can be designed out.

The Clear Picture SA Framework adapts proven principles from aviation, healthcare, and defense — domains where clarity directly affects performance — and applies them to the industrial context. Each concept is described and illustrated through static example designs in Figma or demonstrated directly in industrial visualization platforms, showing how cognitive science can inform better display design.

How SA Levels Guide Design

Each principle supports one or more levels of Situation Awareness, as defined by Mica Endsley’s model:

  • SA Level 1 — Perception: Detecting and displaying the right information, at the right time.
  • SA Level 2 — Comprehension: Interpreting what that information means in context.
  • SA Level 3 — Projection: Anticipating what will happen next if current trends continue.

Designers can use these levels as a cognitive map — ensuring dashboards support not only visibility, but also understanding and foresight.

How the Framework Applies These Concepts

The Clear Picture SA Framework documents how each principle can be expressed in modern industrial visualization tools, using both static examples and interactive demonstrations. Together, these studies illustrate how SA-oriented design reduces cognitive load and improves decision-making in real time.

Current SA Concepts

  • Information Timeliness
    “How current is the information I’m seeing?”
  • Deviation
    “How far am I from where I should be?”
  • Classification
    “Can I group this information into meaningful categories?”
  • ETA
    “How long until a critical state is reached?”
  • Data Confidence
    “How much trust should I place in this data?”