Inventory management: A high-level analysis of selected process elements, and factors impacting plan performance - A case study at Alfa Laval
Bibliographic record
Abstract
Purpose: The purpose of this thesis is to increase the effectiveness of the case company, Alfa Laval’s, inventory management by finding challenges, opportunities, and gaps within its current inventory management practices. Background: An essential part of an organization’s planning and control is inventory management, which helps manage supply and demand uncertainty as well as mismatches in upstream and downstream variables, as small changes in inventory management practices can greatly impact an organization's efficiency and responsiveness. Currently, Alfa Laval, a make-to-order (MTO) company, is implementing a new inventory management initiative, ATHENA, in hopes of reducing the amount of tied-up capital. The initiative focuses on creating a global inventory management process, a set of inventory planning principles, and key performance indicators (KPIs). However, Alfa Laval wants clarity on whether this is comprehensive enough or if there are gaps in the plan. Therefore, the following topics were analyzed: Strategic alignment, Inventory management, Classification, Inventory control, Forecasting, Collaboration, Key Performance Indicators, and Organizational structures. Method: The research method of this master thesis was a single case study, with a single unit of analysis which was Alfa Laval’s inventory management operations. The case study structure allowed a holistic, in-depth analysis of the phenomenon. An abductive research approach was also used as it provided flexibility in the formation and analysis of theories. The data in this thesis is primarily qualitative data from structured and semi-structured interviews. Findings: This study found that Alfa Laval’s ATHENA is a very comprehensive initiative and that the company has an adequate understanding of inventory classification, inventory control, and the usage of KPIs during the follow-up stage as well as the need for strategic alignment and finding a balance between centralized and decentralized organizational structures. However, the findings showed that the plan lacks focus on external variables that enable better inventory management. Primarily, forecasting and collaboration. For example, as an MTO company, Alfa Laval should focus on forecasting for components, as these items have an "independent demand" not directly linked to the unit demand. Additionally, in order to reduce the bullwhip effects on the supply chain and therefore inventory management, Alfa Laval should focus on increasing data quality shared with key suppliers and implement a "key customer" pool for interaction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".