Beyond the Surface of Profit : A Performance Attribution Framework Applied to Epiroc’s Manufacturing Operations
Bibliographic record
Abstract
Accurately pinpointing and understanding how and where profits arise within companies is ofgreat importance for understanding how achieved results should be interpreted. For multi-national organizations, foreign exchange and interest rate risks are a particularly large andunpredictable source of impact on what the profit actually turns out to be. Previous attemptsusing a performance attribution model that can break down profits, changes in daily net presentvalue, has been shown successful. The used performance attribution model incorporates factorssuch as interest rate risk and foreign exchange risk in addition to classic factors such as marginsin explaining where the profit comes from in a way that is numerically exact.In this thesis, the aim is to further develop previous attempts in collaboration with the Swedishmining equipment company Epiroc. This is done by dividing one of their facilities into businessunits and extracting all available data from a four-year period for these respectively, and usingthe data to develop a mathematical model that can link the performance attribution frameworkto an industrial company. Conducting performance attribution on the individual business unitsillustrates how the model can be used to evaluate and interpret where profits originate from.To answer the purpose of the thesis, literature studies on performance attribution and internalpricing methods are carried out. The studies are used to develop an understanding of how acompany can be decomposed into business functions and how internal prices are set for businessunit specific profits. Studies on performance attribution are essential to gain an understandingof how the implemented framework should be used to capture activities of an industrial companyin the context of financial assets and instruments. The literature studies are also performed toensure that the chosen framework is the most appropriate for this study.The idea and aim of the work is based on previous master theses made in the years prior tothis. However, the implementation and mathematical interpretation of an industrial companyin the context of a financial performance attribution framework is entirely new and developedfor this thesis. This to as accurately as possible describe the real world through mathematicsand incorporate extensively larger data sets than in previous attempts.The results of the thesis show that it is possible to conduct performance attribution on specificbusiness units, decomposing and explaining profits in detail on a daily basis. This is demon-strated though applying the implementation of the performance attribution and mathematicalmodel without introducing any significantly large error terms when comparing the results toactual data. Performance attribution is executed on as much data as possible, as well as onsubsets of data to demonstrate the possibilities of use in an industrial context. Showing resultsthat could be of great interest for companies acting on a global market.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".