Navigating challenges and enhancing efficiency in production planning
Bibliographic record
Abstract
BackgroundA well-designed production planning system is the cornerstone of successful manufacturing operations, unlocking the full potential of a manufacturing process.By optimizing production planning processes, companies can achieve efficiency and meet customer demands, in order to stay competitive.This master's thesis explores the complexities and opportunities of production planning in the context of Alfa Laval's assembly factory in Lund. Problem definition and purposeAlfa Laval is currently undergoing significant changes in workshop and flow configuration, which are impacting the phenomenon of planning and control processes.These changes have led to noticeable symptoms.The planning process is perceived as time-consuming, and places strain on multiple resources.Therefore, the purpose of this thesis is to find improvement areas of the planning processes by identifying root causes affecting the issues related to production planning at Alfa Laval. Methodology and Theoretical frameworkTo fulfill the purpose, a case study was conducted.The empirical data was collected through interviews, observations, internal documents and information systems in order to map the current planning process at the company.The literature review was divided into three main parts that are equivalent to production planning levels, Master Schedule, material and capacity plan, as well as production planning and control.In addition, various concepts impacting production planning was investigated into as well. ConclusionThe thesis ultimately resulted in a list of recommendations to the company that covers all planning stages, which is believed to improve the planning process at Alfa Laval.The recommendations at the master scheduling level are in general about consensus and utilizing correct methods.A material and capacity plan was not found and is therefore recommended to implement a suitable one.Lastly, the recommendation at the level of production activity control was in general to refine existing processes.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".