Simulation models and digital twins as the basis for production planning

1/20. Why do designers use “extreme users” in research?

To compare users with the average audience.

To uncover hidden needs by learning from users with unique or intense experiences.

To challenge typical assumptions with random examples.

To exaggerate results and make stories more interesting.

2/20. How can design criteria be used in a project?

To loosely guide the look of the solution.

To create a grading rubric for others.

Only to check off tasks after finishing.

To define success by aligning with user needs, project goals, and context.

3/20. What does divergent thinking aim to achieve in creative work?

To identify the single best solution immediately.

To evaluate existing options thoroughly.

To generate a wide range of possibilities and explore multiple perspectives.

To refine a few selected ideas for execution.

4/20. What is the value of documenting your creative process?

It enables reflection, tracks development, and supports communication of design decisions.

It’s only useful for making presentations.

It helps show your work during evaluations.

It slows you down and isn’t necessary.

5/20. Why is reflection important in creative projects?

To deepen understanding, assess growth, and refine approaches for future work.

To take a break from actual work.

To review final grades or feedback.

To look back on what went wrong.

6/20. What’s a key purpose of visual mapping tools like mind maps or system maps?

To make the project look more colourful.

To list all features of the product.

To clarify complex problems, organize thinking, and identify patterns or opportunities.

To create artful representations of data.

7/20. When is it most helpful to use a storyboard in a project?

Only in filmmaking or video projects.

As a decorative visual for your report.

When exploring how users will interact with a solution across time and touchpoints.

After the solution is complete.

8/20. Why are personas used during design processes?

To synthesize real user research into relatable archetypes that guide design decisions.

To broadly group users for marketing.

To guess what users might like.

To create fictional characters for presentation slides.

9/20. Your facility depends on just-in-time raw materials from a supplier experiencing unpredictable delays. Management wants to test how these disruptions affect production schedules and explore buffer strategies.

Simulate likely delays using average delivery data.

Integrate supplier delivery patterns into a digital twin and simulate mitigation strategies like buffers or rescheduling.

Order excess materials without analysis.

Manually adjust the daily schedule when deliveries are late.

10/20. You’re expanding a high-speed bottling line by adding robotic arms and conveyors. The goal is to boost throughput without disrupting existing operations. Leadership wants to validate that the new equipment will not create new bottlenecks or increase downtime during peak hours.

Use a comprehensive simulation model to analyse the impact of robots, test different layouts, and identify optimal configurations.

Use static estimates and spreadsheet calculations to forecast output.

Simulate robot performance only, without integrating upstream/downstream processes.

Install all new equipment first and observe results during production.

11/20. Your facility has a recurring issue with unplanned equipment breakdowns causing downtime. Management wants a system to proactively detect and address potential failures before they disrupt production.

Continue with reactive maintenance after failures occur.

Increase frequency of fixed-schedule preventive maintenance.

Deploy digital twins with sensor data to track conditions and implement predictive maintenance.

Use historical failure records to estimate time-between-failures.

12/20. During heatwaves, your equipment performance drops due to inadequate cooling. This impacts quality and uptime. You’re asked to find long-term solutions for climate resilience.

Simulate airflow in problem areas using historical temperatures.

Install extra fans and wait for results.

Connect climate sensors to a digital twin and simulate cooling adjustments under various weather conditions.

Ignore weather conditions and maintain regular operations.

13/20. The facility just received a new, complex multi-axis CNC machine. Operators are unfamiliar with its interface and fail frequently during operations, leading to scrap and tool damage. You want to introduce digital twin-based virtual training modules.

Develop interactive digital twin environments with real machine behaviour, errors, and adaptive learning scenarios.

Let operators learn by trial and error on the live machine.

Offer 3D virtual models of the machine for basic training.

Provide written manuals and static 2D tutorials.

14/20. The factory’s electricity bills are rising, especially during peak production hours. You suggest using digital twins to analyse energy usage, detect inefficiencies, and propose smarter energy scheduling to cut costs and reduce environmental impact.

Develop a digital twin with real-time energy sensors to simulate usage patterns, test alternative schedules, and apply AI to recommend improvements.

Build a simulation using historical data to identify patterns and optimize scheduling.

Assume uniform energy use and apply arbitrary reductions to all machinery.

Analyse monthly bills manually and suggest time-based operation reductions.

15/20. The production line has experienced an unexplained drop in daily output. You suspect a bottleneck near the packaging station but lack concrete data. Management wants a quick and cost-effective diagnostic method.

Interview line workers to speculate on where delays are happening.

Use a calibrated simulation model based on real-time data to pinpoint bottlenecks and test process alternatives.

Simulate packaging station timing using average cycle data.

Replace packaging equipment immediately and observe if output improves.

16/20. Your factory is transitioning from batch production to on-demand customization. Management wants to evaluate how this shift affects scheduling, tooling changeovers, and material handling.

Adjust only a few workstations to accommodate variants.

Simulate product variants with average processing times.

Build a simulation to model individual custom flows, changeovers, and resource allocation.

Apply the existing production plan to customized orders.

17/20. Why is virtual commissioning, based on digital twin technology, crucial during the implementation of new industrial equipment, and what are its primary advantages in reducing deployment risks?

It prevents integration between automation software and hardware during the setup phase of production lines.

It avoids simulation entirely by using static control system representations in real-world testing environments.

It delays performance verification until after physical installation to ensure resource savings and reduced complexity.

It validates processes virtually before installation, allowing early issue detection and operator training in safe conditions.

18/20. What ethical considerations should be prioritized when implementing digital twin systems in industrial environments to ensure transparency, fairness, and responsible data handling practices across stakeholders and departments?

Collecting all operational data without restrictions or privacy compliance to enable unrestricted predictive analysis.

Centralizing algorithmic control in external networks, avoiding internal verification of computational ethics.

Emphasizing algorithmic transparency, informed stakeholder consent, and mitigation of potential data bias in decision processes.

Maximizing automation regardless of social or employment impacts on technical operators and employees.

19/20. How does discrete event simulation differ from continuous modelling approaches when applied to industrial production analysis and process optimization efforts?

It ignores event scheduling and models all variables as static equilibrium systems.

It represents system changes as a sequence of discrete, time-dependent events for accurate process optimization.

It assumes all production flows occur simultaneously without time or sequence variations across processes.

It replaces real-time monitoring with historical data aggregation from independent production stages.

20/20. What are the main responsibilities of a simulation analyst when developing a computer-based model to represent, test, and validate industrial production processes for performance improvement and efficiency gains?

Developing physical prototypes to complement simulation software and ensure tangible operational reference.

Managing all human resource tasks and financial allocations during system implementation.

Supervising marketing campaigns promoting industrial automation software within production networks.

Constructing, validating, and verifying models that accurately mirror operational behaviour and resource interactions.

Your result: /100

You have a Low Readiness Index, indicating that you/your organization is in the early phases of embracing simulation models and digital twin technologies for production planning. Existing processes may still depend largely on manual methods or basic digital tools, lacking the use of real-time data or sophisticated modeling techniques. This limitation affects your capacity to foresee disruptions, optimize resources, or create efficient workflows. When simulation is employed, it tends to be isolated and not integrated with operational systems, while the concept of digital twinning has yet to be established.

Actions to enhance your Readiness Index:

Initiate trials with simple simulation tools to model critical processes.
Start basic data collection from production lines to aid digital modeling.
Provide introductory training to employees about the concepts of simulation and digital twins.

You have achieved a Moderate Readiness Index. You/Your organization recognizes the advantages of simulation models and digital twins, with some initiatives already in progress. For instance, you might utilize simulation to assess production scenarios or have limited digital representations of assets. However, integration is sporadic, and many decisions are still made without real-time data insights. While there may be exploration of virtual commissioning or predictive analysis, these methods have not yet been thoroughly implemented throughout the organization.

Actions to enhance your Readiness Index:

Broaden the use of simulation to encompass multiple processes and connect results to performance metrics.
Invest in pilot projects for digital twins that link real-time sensor data with virtual models.
Enhance collaboration across departments to integrate simulation and digital twins into planning processes.
Develop digital competencies within the workforce through specific training programs.

You have achieved a High Readiness Index. You/Your organization demonstrates a robust integration of simulation models and digital twins in production planning. You employ real-time data, predictive analytics, and advanced modeling techniques to create efficient workflows, optimize resources, and anticipate disruptions. Methods like virtual commissioning, predictive maintenance, and scenario analysis are systematically implemented, and digital twins support strategic planning, sustainability goals, and innovation. This level of readiness positions you as a front runner in Industry 5.0, merging human-centric strategies with advanced technologies to achieve resilience and sustainability.

Actions to enhance your Readiness Index:

Expand the application of digital twins across the entire production lifecycle.
Seek certifications and frameworks that validate maturity in digital innovation.
Share successful practices both internally and externally to reinforce leadership in Industry 5.0.
Continue investing in AI-driven optimization, sustainability initiatives, and workforce empowerment.

EQF level alignment

According to your results, your current competence level can be estimated as %EQF%.

Back

Simulation models and digital twins as the basis for production planning