Integration of acquired companies - case sudies to create a framework for future acquisitions in Alfa Laval
Notice bibliographique
Résumé
This Master’s thesis is performed on behalf of Alfa Laval, a multinational company providing technological products and services. Alfa Laval has in the past had a lot of successful mergers, but there were also those cases that led to sell off and those that had potential to add more value than was actually realized. In the common case, it has been shown that 60-70 per cent of M&As fall short when it comes to realizing the intended synergies. Facts on international M&As show that shareholder value added increased in less than one of five cases. Mainly, I got directives from Alfa Laval to look into Alfa Laval’s merger ventures in the past. To get a good depth on my findings, interviews were conducted on main players in real cases as a pre-study. I could then see patterns that can be changed and optimized and created an AL-acquirer to use in search for a good merger partner and an AL-in, Plan of Action to follow the integration through to fulfil the purpose of the study. The purpose of this Master’s thesis is twofold – the first part consisting of: • Case studies and mapping of two acquisitions in Alfa Laval This first part aims at identifying crucial factors that can be changed and optimized for superior integration in future M&A and to manage multiple acquisitions in Alfa Laval. This through on site observations, interviewing and analysis of outcomes on synergies realized and value added. The second part has the purpose of: • Developing tools for companies to use in search for the right merger partner and for integration in managing merger ventures On the base of the first part, I chose the input and methods that Alfa Laval can use in M&A. The tools are constructed to be helpful throughout the whole organization and to give guidance on how to face a newly acquired company. The AL-acquirer is mainly of use before an acquisition and the AL-in, Plan of Action shall be guidance during the integration phase. I chose to use the cases and interview as well as literature studies as a pre-study in the creation of my tools. My findings tell me, that after evaluating that the acquisition is in line with Alfa Laval’s strategy to grow, then vALue Added by Acquired Company and Similarity with Parent Company, ALikensess, are the two most important criteria in search for the right merge partner, comprising several different factors, including communication, products, personnel, company culture etcetera. High values on these criteria provide greater possibility to succeed in a merge and gives more room for risk taking. But also those companies that score low in some areas can be a potential partner – presupposing that special caution is needed and that the AL-in, Plan of Action is followed through thoroughly for Operations IS/IT, Communication, Products, Management, and Human Resources. Main observation lies in the final study of economic value and the price paid to count for that the value added is put in perspective of the price paid and future potential in Alfa Laval. While working on this Master’s thesis I found that simple methods for real life methods in the M&A area are not to be found. I have taken the first step towards developing methods that will help those concerned with M&A in Alfa Laval and to provide knowledge that enables transfer of experience on such deals
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,005 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,004 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,002 | 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 ».