Beyond the Surface of Profit : A Performance Attribution Framework Applied to Epiroc’s Manufacturing Operations
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,003 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».