Preparing for a Digital Twin at Alfa Laval
Bibliographic record
Abstract
Problem description: Alfa Laval, a global leader in providing solutions for heat transfer, separation, and fluid handling, seeks to improve production scheduling at their component factory in Lund. They aim to digitalise the process by developing a digital twin (DT), a virtual model of the manufacturing system. The DT will simulate production flow to support decision-making and optimise scheduling. Before implementation, Alfa Laval wants to understand how the DT can add value to their operations and identify the key factors and challenges associated with its adoption. Purpose: The purpose of this thesis is to develop a base model for a digital twin, a digital model, for the small pressline of plates at Alfa Laval’s Global Core Component Factory in Lund. The main goal is to explore the potential of a simheuristic approach for improving productivity, specifically in terms of increasing throughput and decreasing setup time. The model will primarily support the planners responsible for short-term scheduling. The research will also focus on evaluating key factors and challenges of using the created digital model for production scheduling at Alfa Laval. Methodology: The study combines exploratory and problem-solving research strategies to optimise production scheduling using an operations research framework. The research follows four key steps: defining the problem through data collection and interviews, formulating a model, developing a simulation model with genetic algorithms and testing the model for accuracy and reliability. A literature review supports the methodology and further analysis ensures robustness. Conclusions: The thesis resulted in the development of a digital model of Alfa Laval’s small pressline and a new optimisation process. The model offers enhanced visibility, efficiency and decision support in short-term production planning. However, challenges include planning complexity, data accuracy and integration. The model must evolve from a digital representation to a fully integrated DT with real-time data exchange to unlock its full potential.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".