Product Design: Scenario-Based for Smarter Decisions in Digital Twin

The Situation 

Industrial data systems are powerful yet complex — and decision-makers often struggle to translate data into action.

Our objective was to design a more intuitive way for users to experiment, simulate, and understand possible outcomes before making critical decisions.

The existing workflows required constant switching between dashboards and tools, which supported technical accuracy but not clarity.

Through discovery and iteration, we transformed fragmented workflows into a guided, scenario-based experience that made complex analysis both accessible and interactive.

Design Context

Traditional digital-twin tools visualise “what is” — live data and historical states , but real-world decisions depend on exploring “what could be.”

During discovery, we explored how a scenario-driven approach could allow engineers to test assumptions, simulate consequences, and evaluate alternatives directly inside a unified workspace.

SIM 4200 Amine System – TSC Simulation

The Discovery Phase — Finding the real problem

Interviews and workflow observations revealed that engineers were:

• Manually cross-checking results across multiple tools.

• Re-running simulations outside the main platform, then re-importing results.

• Unsure whether baseline data was reliable enough to trust.

Insight: Users didn’t need more visualisation — they needed a safer, guided space to think through decisions.

Mapping these insights produced a user flow connecting every step of the decision process — discover → observe → create → explore → compare → finalise — highlighting friction points in data validation and comparison.

Defining the Scenario Concept

A scenario allows users to adjust parameters such as temperature, production rate, or maintenance schedule and immediately see projected outcomes.

Each scenario acts as a temporary, non-destructive layer:

• It can be saved, compared, or shared.

• It encourages experimentation within safe boundaries.

• It enables users to ask “what if ?” and instantly see the impact — all inside the same visual environment.

Use cases

Use Case 1 : Multi-level drill-down visualization

Goal:

Enable users to navigate 2D/3D visuals with drill-down capability across multiple levels (e.g., plant, train, equipment).

Flow Summary:

• User opens visualization at the top (plant) level to see statuses and datasets.

• User drills down from plant → train → unit → equipment level.

• Each level shows relevant pre-set statuses, monitored conditions, and datasets.

Use Case 2 : Scenario visualization with simulation models

Goal:

Allow users to view, set up, and trigger simulation runs within the same visualization environment.

Flow Summary:

• User logs in to view simulation models.

• User sets up a scenario in the interface, selecting a point in time and adjusting variables.

• The simulation runs and results are visualized in the platform.

Use Case 3 — Integration of data sets

Goal:

Provide users with access to associated process data and contextual information linked to equipment or assets.

Flow Summary:

• From the visualization, user selects equipment to access related datasets (e.g., logs, lab data, reports).

• Some data is summarized directly; full datasets can be opened in an external interface.

Use Case 4 — Aggregated key information

Goal:

Display aggregated KPIs and alarms across multiple levels for situational awareness.

Flow Summary:

• User opens visualization at the plant level to see KPIs and alarm heatmaps.

• User navigates progressively (plant → train → unit → equipment) to view KPIs relevant to each level.

Our core challenge:

The challenge was to create a unified, intuitive workflow for scenario assessment without overloading users.

So we started from first principles:

1. One entry point: All scenario creation — baseline, what-if, or assessment — starts from a single menu.
2. Step-by-step flow: Users move through a logical sequence — decide → collect → assess → compare.
3. Clarity of states: The interface always answers “Where am I?” and “What happens next?”

Goal: To reduce cognitive load and support fast, reliable decision-making in a complex industrial environment.

Whiteboard sessions and early prototypes with stakeholders helped visualise how scenario building would work in practice.

The flow evolved into five clear phases:

• Discover: Locate relevant data or process.
• Observe: Check real-time or historical data for readiness.
• Create: Define or modify baseline conditions.
• Explore: Run simulations and compare outcomes.
• Decide: Review results and communicate actions.

This systematic structure balanced precision with simplicity, making the tool effective for both expert engineers and occasional users.

Key takeaways from iterative testing:

• Users valued consistency over flexibility; too many paths caused confusion.

• Trust in the baseline was more critical than the scenario itself.

• A visible progress indicator made complex flows feel predictable.

The design evolved to simplify menus and align interaction logic with user reasoning. Early collaboration and testing helped the final solution match users’ mental models and expectations.