Course: Industry 4.0

Assignment A2.1 - Creating a Digital Twin

Assignment Instructions

Please note: this is an individual assignment.

During this second week, we have learned a lot about digital twins. As we have already seen, it is a crucial tool to transform multiple industries such as manufacturing, but also the energy, aerospace, automotive, and retail industries, among others.

This assignment is divided into two parts. Part 1 is intended to help you get started by reflecting openly on key concepts and ideas. This initial exploration will provide a foundation for the more applied work required in Part 2. Both components are integral to the final submission and will contribute meaningfully to your overall performance.

Exercises and Questions to Answer

Part 1:

Discussion of Industry 4.0 and shift to Industry 5.0. Write a short reflective essay (300-400 words) addressing the following points. Your response should critically explore how emerging technologiesespecially Generative AI and Large Language Models (LLMs)are influencing the transition from Industry 4.0 to Industry 5.0, considering both their transformative potential and ethical implications.

• Discuss how the technologies and principles of Industry 4.0such as IoT, robotics, automation, and traditional AIcan be reoriented to support Industry 5.0 values in the situation or challenge you used the digital twin activity for. Analyze how the introduction of Generative AI might enhance or challenge this shift, particularly in terms of human-machine collaboration, decision-making, and ethical responsibility.
• Provide two real-world examples or plausible future scenarios in which Industry 5.0 values transform manufacturing operations for the challenge you discussed and created the digital twin for. In each case, describe how departments (e.g., R&D, production, HR), data flows, and systems are involved. Consider the role that LLMs or similar tools could play in these contexts (e.g., in simulation, design support, or training), and reflect on any ethical or operational challenges they might introduce.

Your answers to all questions should demonstrate practical understanding, technical awareness, and strategic thinking in the context of real-world industrial innovation.

Part 2:

Task: Based on your current company or a previous organization you know well, complete the following task by designing a Digital Twin use case to solve a real operational challenge:

Identify a relevant production or operational challenge from your current or past work experience. Examples might include:

• Unplanned equipment downtime
• Low product customization capability
• Inventory management inefficiencies
• Energy consumption variability
• Quality control inconsistencies

Design a Digital Twin concept to address this challenge. Your proposal should include:

• Type of Digital Twin: Specify whether it is a Product Twin, Facility Twin, or Process Twin.
• Data sources: Describe the data that would be collected (e.g., sensors, ERP systems, machine logs, customer feedback) and how it would feed into the twin.
• Simulation or analysis capabilities: Explain what the Digital Twin would enable (e.g., predictive maintenance, layout optimization, real-time monitoring, demand forecasting).
• AI integration: Describe how AI techniques (e.g., predictive analytics, anomaly detection, computer vision, generative simulation) could enhance the twin's effectiveness and decision-making value.

Justify the ROI of your solution. A frequent barrier in Digital Twin projects is justifying the investment in technology, infrastructure, and organizational change. Provide a reasoned argument for the potential business value of your proposed solution by addressing:

• Expected efficiency or cost savings
• Risk mitigation (e.g., fewer breakdowns, reduced waste)
• Strategic advantages (e.g., agility, better forecasting, customer satisfaction)
• Timeframe to impact and scalability

Assignment Deliverables

Deliverable:

Prepare a 2-page structured summary that clearly presents your answers for Part 1 and 2 of the assignment. It must include the following sections:

1. Identified Challenge. Briefly describe the production or operational issue in your organization that the Digital Twin will address.
2. Type of Digital Twin. Indicate whether it is a Product Twin, Process Twin, or Facility Twin, and justify your choice.
3. Data & Technology Stack. Summarize the key data sources (e.g., sensors, systems, logs), technologies (e.g., AI models, simulation tools, cloud infrastructure), and integrations needed to implement the Digital Twin.
4. Target KPIs and ROI Justification. Specify the expected Key Performance Indicators (KPIs) that your solution aims to improve (e.g., downtime reduction, throughput increase, waste minimization).
5. Include a brief justification of the return on investment (ROI), considering potential business value, efficiency gains, and time-to-impact.
6. Diagram or Flowchart (Recommended). Include a simple diagram, architecture sketch, or flowchart to visually represent your solution. This is optional but highly recommended to support clarity and communication.
7. Discussion of Industry 4.0 and shift to Industry 5.0. Include a brief essay (200-300 words)

Grading

Your assignment will be graded on how well your answer covers each of the sections above (see rubric for details).

Rubric

Rubric Assignment A2.1

Criteria	Ratings	Pts
This criterion is linked to a Learning OutcomeUnderstanding of Industry 5.0	5 ptsExcellentDemonstrates deep understanding of Industry 5.0 objectives and how they build upon Industry 4.0; clearly integrates the role of Generative AI. 3 ptsGoodSolid understanding with minor gaps or less depth; some mention of Generative AI. 2 ptsBasicPartial or unclear understanding of objectives. 0 ptsNo MarksNo clear understanding demonstrated.	5 pts
This criterion is linked to a Learning OutcomeAnalysis of Industry 4.0 evolution	5 ptsExcellentClearly analyzes how Industry 4.0 principles evolve toward Industry 5.0, including the role and ethical implications of Generative AI. 3 ptsGoodEstablishes the connection but lacks depth or nuance. 2 ptsBasicVague or shallow mention of evolution. 0 ptsNo MarksFails to develop any relevant analysis.	5 pts
This criterion is linked to a Learning OutcomeApplied examples or scenarios	5 ptsExcellentProvides two relevant, detailed, and realistic examples with a clear role of technologies and departments. 3 ptsGoodExamples are adequate but less developed or contextualized. 2 ptsBasicExamples are unclear or generic. 0 ptsNo MarksNo valid examples provided.	5 pts
This criterion is linked to a Learning OutcomeCritical and ethical reflection	5 ptsExcellentShows strong critical thinking and ethical awareness in the use of AI in industry. 3 ptsGoodIdentifies some risks and opportunities with acceptable reasoning. 2 ptsBasicLimited or underdeveloped reflection. 0 ptsNo MarksNo critical or ethical reflection.	5 pts
This criterion is linked to a Learning OutcomeIdentification of operational challenge	8 ptsExcellentClear, relevant, and well-contextualized challenge. 5 ptsGoodUnderstandable challenge with limited context. 2 ptsBasicVague or superficially described challenge. 0 ptsNo MarksNo valid challenge identified.	8 pts
This criterion is linked to a Learning OutcomeType of Digital Twin and relevance	8 ptsExcellentCorrectly selected type, well justified in relation to the challenge. 5 ptsGoodValid type with general justification. 2 ptsBasicUnclear or poorly justified type. 0 ptsNo MarksType not specified or poorly defined.	8 pts
This criterion is linked to a Learning OutcomeData sources and technology stack	8 ptsExcellentPrecise and coherent description of required data and technologies. 5 ptsGoodAdequate identification with minor limitations. 2 ptsBasicSuperficial or incomplete description. 0 ptsNo MarksNo identification of data or technologies.	8 pts
This criterion is linked to a Learning OutcomeSimulation/analysis capabilities	8 ptsExcellentClear use of simulation/AI to provide tangible value. 5 ptsGoodAdequate use but underdeveloped. 2 ptsBasicUnclear or low applicability. 0 ptsNo MarksNo functional description.	8 pts
This criterion is linked to a Learning OutcomeROI justification and KPIs	8 ptsExcellentConvincing argument with measurable impact and well-defined KPIs. 5 ptsGoodReasonable justification with some limitations. 2 ptsBasicWeak or poorly connected justification. 0 ptsNo MarksNo ROI justification.	8 pts

Total Points: 60