Designing a New Warehouse to Improve Space Utilization and Handling Efficiency: A Case Study of a Production Warehouse
Bibliographic record
Abstract
Title: Designing a new warehouse to improve space utilization and handling efficiency: A case study of a production warehouse Authors: Emil Niklasson, Erik Ramström Supervisor: Joakim Kembro, Department of Industrial Management and Logistics, Faculty of Engineering, Lund University Problem description: The focus on warehousing has grown in the industry during the 21st century to increase the competitive advantage of companies. Despite this, the development of the coil warehouse at Alfa Laval has received little focus during the last decades. This is in contrast to Alfa Laval's goal of following global industrial development. Purpose: To close this gap, the purpose of the thesis is to provide two recommendations of how the production warehouse of coils can be designed to increase space utilization and make the handling process more efficient. Objectives: The recommendations to Alfa Laval are provided through fulfilling the objectives of the thesis. The first objective is to describe the current situation to get an understanding of the processes needed and which changes that are suitable. The second objective is to identify the challenges with storage of coils to know what the new solution should be able to solve. The third objective is to identify the contextual factors to take the unchangeable parameters into account. The fourth and final objective is to identify suitable configurational elements which the final recommendations will be based on. Methodology: The method used in this thesis is the case study which consists of analyzing the problem in the context of a case company, Alfa Laval. The case study began with a literature review to understand what previously had been written about warehousing. Following this, the current situation at Alfa Laval was mapped through observations and interviews of employees as well as data extraction from information systems. Finally, the collected data was analyzed to identify the suitable configurational elements which were combined into two recommendations. Conclusion: The result of the thesis is two recommendations which both decrease the majority of the identified challenges. Both of the recommendations have the same changes in the operations with a movement of the quality inspection and unpackaging from the picking phase to the receiving phase, to make it possible to pick directly to the production, and with an introduction of more structured picking and storage policies. The storage of the first recommendation is to use cantilever racks and to store the coils without pallets. The second recommendation is to automate the picking and put-away process through installation of an overhead crane with the coils stacked on the ground without aisles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".