Streamlining the Ordering Process: A Case Study of Alfa Laval
Bibliographic record
Abstract
Background One of Alfa Laval’s factories in Lund, LA, has noticed that up to 50% of all order proposals suggested by their ERP system JEEVES is considered to be irrelevant and are subsequently disregarded by the purchasers. Consequently, many purchasers disregard the parameters in JEEVES that control what proposals get suggested and instead order on “gut-feeling” and personal experience. Not only does this create problems because of a lack of objective parameters, but it is also a waste of purchasers’ time to go through and disregard proposals every day. Alfa Laval has therefore expressed an interest in increasing the accuracy and reliability of the proposals suggested by JEEVES, as well as decreasing the time of the order proposal process. Purpose The purpose of this thesis is to evaluate Alfa Laval’s ordering process in order to increase the reliability and accuracy of the order proposals, as well as reduce the amount of time spent handling said order proposals. Research Questions • How is the current order proposal process organized? • How can order parameters be improved to increase their reliability and accuracy, and decrease order proposal handling time? • How can items be categorized to increase the reliability of order proposals? Method This thesis uses a case study approach with an explanatory research purpose, as the penultimate goal is to evaluate and improve the ordering process at Alfa Laval. Furthermore, a combination of qualitative and quantitative data was used in order to gather a comprehensive view of the current processes and order parameters at Alfa Laval. Findings Many of the parameters in JEEVES are inaccurate due to negligence and because they have not been updated since their introduction. Suggestions for improving said parameters are provided, and implications discussed. Moreover, it is proposed that Alfa Laval should divide their items into three different categories and employ different approaches for each category. The combination of these two proposed improvements means that Alfa Laval will increase the accuracy and reliability of their proposal parameters as well as reduce the number of proposals and overall time spent on handling order proposals.
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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 | 0.013 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".