Lead time analysis and reduction at Alfa Laval DC Lund
Bibliographic record
Abstract
Alfa Laval DC Lund is both a spare part manufacturer and distributer. The spare parts that Alfa Laval DC Lund supplies are used for the heat transfer business unit of Alfa Laval. Sometimes orders need to be produced fast as customers may have breakdowns in production and sometime orders arrive months earlier than customers want to receive delivery as scheduled services occur at the customer. Problem A problem today is that the time it takes Alfa Laval DC Lund to produce and send an order to a customer is perceived as too long. It is not known where in the process the majority of time is spent as well as what can be done to reduce it. Purpose The purpose is to perform a thorough analysis of the lead time for manufacturing orders from that the customer order arrives at Alfa Laval DC Lund to the customers receive delivery. Objective A solution that will reduce lead time for manufacturing orders should be created and implemented. Deliverables The project should deliver savings/profit of 5000 euro per year and/or a process improvement of 25%. Methodology The research has had a systems approach which was helpful to provide a holistic perspective. A combination of a case study and action research has been used to build a thorough understanding of the business before trying to improve it. Data has been gathered through interviews, observations, literature studies and from measuring processes and extracting data from the ERP system’s database. During the research, emphasis has been on ensuring that reliable and valid data has been used. Results The production lead time data in Movex was adjusted to better fit the actual production lead time. The result was a decrease in lead time which could be seen directly after implementation. Conclusion The benefits of the implemented solution will be a 30% decrease in internal lead time when material is available from start and a 10% decrease in internal lead time when material is not available from start. This will in turn generate a total of approximately 14 000 euro per year in savings from less tied up capital and profits from earlier revenue. The analysis has also yielded information that Alfa Laval DC Lund can use to start new projects with the purpose to reduce lead time and/or improve their business. It has been concluded that human interference with as well as wrong data in the ERP system drives lead times. The research also demonstrates the importance of working with the ERP system and using its features in a correct way instead of working beside and overriding it.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".